A DELAY-DEPENDENT APPROACH TO DESIGN STATE ESTIMATOR FOR DISCRETE STOCHASTIC RECURRENT NEURAL NETWORK WITH INTERVAL TIME-VARYING DELAYS
|
|
- Natalie Neal
- 5 years ago
- Views:
Transcription
1 ICIC Express Letters ICIC International c 2009 ISSN X Volume, Number (A), September 2009 pp A DELAY-DEPENDENT APPROACH TO DESIGN STATE ESTIMATOR FOR DISCRETE STOCHASTIC RECURRENT NEURAL NETWORK WITH INTERVAL TIME-VARYING DELAYS Chin-Wen Liao, Chien-Yu Lu, Kai-Yuan Zheng and Chien-Chung Ting Department of Industrial Education and Technology National Changhua University of Education No.1, Jin-De Road, Changhua 500, Taiwan lcy@cc.ncue.edu.tw Received March 2009; accepted May 2009 Abstract. This paper deals with the problem of state estimation for discrete stochastic recurrent neural network with interval time-delays. The activation functions are assumed to be globally Lipschitz continuous. Attention is focused on the design of a state estimator which ensures the global stability of the estimation error dynamics. A delay-dependent condition with dependence on the upper and lower bounds of the delays is given in terms of a linear matrix inequality (LMI) to solve the neuron state estimation problem. When this LMI is feasible, the expression of a desired state estimator is also presented. In addition, slack matrices are introduced to reduce the conservatism of the condition. A numerical example is provided to demonstrate the applicability of the proposed approach. Keywords: Recurrent neural network, Stochastic systems, Linear matrix inequality, State estimators, Interval time-delays 1. Introduction. In the past few decades, recurrent neural networks (RNNs) have been intensively studied. Many researchers found successful various applications in many fields such as pattern recognition, image processing, optimization problems, and associative memory. Many of these applications heavily depend on the dynamic behaviors. In practical condition, delayed systems are often encountered and time delay is frequently a source of instability and oscillations in the system. Therefore, dynamics in a neural network often have time delays due to lots of reasons, such as the finite signal propagation time in biological networks and the finite switching speed of amplifiers in electronic neural networks. The situation of time delay could make RNNs have bad performance, and even make system instable. So, many researchers focus on stability analysis for delayed RNNs. A lot of literatures of this issue have been reported in literature [1-11]. State estimation is a subject of great practical and theoretical important which has received much attention in recent years [12-19]. Since, the neuron states are not always fully available in the neural networks outputs in many practical applications. In this kind of cases, it is necessary to estimate the neuron states through measurements. Through available output measurements, many problems are about to estimate the neuron states in which the dynamic of the estimation error is asymptotically globally or exponentially stable. Recently, the state estimation problem for recurrent neural networks with time delays was studied in [12-1], where an effective linear matrix inequality (LMI) [20] approach was developed to solve the problem. The state estimation problem for recurrent neural networks with mixed time delays has been dealt with in [15-17], where sufficient conditions for the existence of estimator have been presented in terms of LMIs. A class of Markovian recurrent neural networks with mixed time delays was presented, where the neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov chain in [18]. [19] presented the design problem of state 5
2 C.-W. LIAO, C.-Y. LU, K.-Y. ZHENG AND C.-C. TING estimator for a class of neural networks of neutral-type with interval time-varying delays, where a sufficient condition for existence of state estimator for the networks is given in terms of LMI. However, it should be pointed out the aforementioned results are continuous delayed RNNs. Recently, the problem of state estimation for discrete-time recurrent neural networks with interval time-varying delay was considered in [21], where a sufficient condition with dependence on lower and upper bounds of delay was proposed and an LMI approach was developed. So far, no state estimation results on discrete stochastic recurrent neural network with interval time varying delays are available in the literature, and remain essentially open. The objective of this paper is to address this unsolved problem. In this paper, the aim is to deal with the state estimation problem for discrete stochastic recurrent neural network with interval time-varying delays. The interval time-varying delay includes both lower and upper bounds of delays. A delay-dependent condition for the existence of estimators is proposed and the criterion is formulated in accordance with an LMI, which introduces into slack matrices and reduces the conservatism of the criterion. A general full order estimator is sought to guarantee that the resulting error system is globally asymptotically stable. Desired estimators can be obtained by the solution to certain LMIs, which can be solved numerically and efficiently by resorting to standard numerical algorithms [20]. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed method. 2. Problem Statement and Preliminaries. Consider the following discrete stochastic recurrent neural network with interval time-delays described by x(k + 1) = Ax(k) + W 1 f(x(k) + W 2 f(x(k τ(k)) + [C x(k) + C h x(k h(k))]ω(k), (1) where x(k) = (x 1 (k), x 2 (k),, x n (k)) T is the state vector, A = diag(a 1, a 2,, a n ) is real constant with entries a i < 1, i = 1, 2,, n, W1 n n and W2 n n are the interconnection matrices representing the weighting coefficients of the neurons, C and C h are known real constant matrices. f(x(t)) = [f 1 (x 1 (t)),, f n (x n (t))] T R n is the neuron activation function with f(0) = 0, τ(k) and h(k) are the time-varying delay of the system satisfying τ m τ(k) τ M, k N, (2) h m h(k) h M, k N, () where 0 < τ m < τ M and 0 < h m < h M are known integers. ω(k) is a scalar Wiener process (Brownian Motion) defined on a complete probability space (Ω, F, P ) which is assumed to satisfy E{ω(k)} = 0, E{ω 2 (k)} = δ, k = 0, 1, 2,, () where δ > 0 is a known scalar. In order to establish main results, it is necessary to build the following assumption which the activation function (1) is assumed to bounded and satisfy the following assumption. Assumption 2.1. The neuron activation functions in (1), g i ( ), satisfy the following Lipschitz condition. 0 g i(x) g i (y) α i, (i = 1, 2,, n), (5) x y where G i R n m are known constant matrices such that x, y R and x y. Assumption 2.2. The neuron activation functions in (1) are bounded. In order to observe the neuron states. The recurrent neural network measurements are assumed to satisfy y(k) = Dx(k) + [Ex(k) + E h x(k h(k))]ω(k), () where y(k) R m is the measurement output and D, E and E h are constant matrix with appropriate dimension.
3 ICIC EXPRESS LETTERS, VOL., NO., For (1) and (5) of the system, we consider the following full-order estimator ˆx(k + 1) = Aˆx(k) + W 1 f(ˆx(k) + W 2 f(ˆx(k τ(k)) + [C ˆx(k) + C hˆx(k h(k))]ω(k) +L[y(k) Dˆx(k) (Eˆx(k) + E hˆx(k h(k)))ω(k)], (7) where ˆx(k) is estimator of the neuron states and L R m n is the estimator gain matrix to be determined. In this article, we found a suitable L R m n such that ˆx(k) approaches x(k) asymptotically. Let e(k) = x(k) ˆx(k) (8) be the state estimator error. Then with (1), (5) and (), the state error e(k) satisfies the following equation: e(k + 1) = (A LD)e(k) + W 1 f(e(k)) + W 2 f(e(k τ(k))) +[(C LE)e(k) + (C h LE h )e(k h(k))]ω(k), (9) where f(e(k)) = f(x(k)) f(ˆx(k)), f(e(k τ(k))) = f(x(k τ(k))) f(ˆx(k τ(k))), e(k h(k)) = x(k h(k)) ˆx(k h(k)). It is obvious to find out from Assumption 2.1 that the solution of (1) exists for all k 0 and is unique.. Main Results. In this section, an LMI based condition will be established. The globally delay-dependent state estimation condition given in (9). We used the LMI approach to solve the estimator gain matrix if the system (9) is globally asymptotically stable. Now, we derive the conditions under which the neural network dynamics of (1) is globally stable. For mathematical formulation, we define Z 1 = ρ 1 P, Z 2 = ρ 2 P, Z = ρ P, Z = ρ P, Y = P L, (ρ 1, ρ 2, ρ, ρ, are given scalars). The following theorem reveals to solving the state estimation problem formulated involving several scalar parameters. Theorem.1. Under Assumption 2.1 and Assumption 2.2, given scalars 0 τ m < τ M, 0 h m < h M, the network output (), the error-state dynamics (9) and system (1) with interval time varying delays τ(k) and h(k) satisfying (2) and () is globally asymptotically stable. If there exist matrices P > 0 i > 0 (i = 1, 2, ), Z i > 0 (i = 1, 2,, ), and diagonal matrix R i > 0, R 2 > 0 and S i, H i, T i, Γ i, Φ i, Θ i (i = 1, 2,, ) of appropriate dimensions such that the following LMI holds Ω τ M S τ Mm H τ Mm T h M Φ h Mm Γ h Mm Θ Ā C τ M S T τ M Z τ Mm H T 0 (τ Mm )(Z 1 + Z 2 ) τ Mm T T 0 0 τ Mm Z h M Φ T h M Z h Mm Γ T h Mm (Z + Z ) h Mm Θ T h Mm Z 0 0 Ā T P 0 C T δ 1 P τ M Ā T 1 τ M CT 1 τ Mm Ā T 2 τ Mm CT 2 h M Ā T h M CT h Mm Ā T h Mm CT
4 8 C.-W. LIAO, C.-Y. LU, K.-Y. ZHENG AND C.-C. TING τ M Ā 1 τ M C1 τ Mm Ā 2 τ Mm C2 h M Ā h M C h Mm Ā h Mm C τ M Z δ 1 τ M Z τ Mm Z δ 1 τ Mm Z h M Z δ 1 h M Z h Mm Z δ 1 h Mm Z < 0 where Ω = Ω(i, j), i = 1,, 9, j = 1,, 9, Ω 11 = (τ M τ m + 1)Q 1 + Q 2 + Q + Q + Q 5 + Q P + S 1 + S1 T + Φ 1 + Φ T 1, Ω 12 = S 1 + S2 T + H 1 + T 1, Ω 1 = S T + H 1, Ω 1 = S T T 1, Ω 15 = Γ 1 + Φ T 2 Φ 1 + Θ 1, Ω 1 = Γ 1 + Φ T, Ω 17 = Θ 1 + Φ T, Ω 18 = Σ T R1 T, Ω 19 = 0, Ω 22 = Q 1 S 2 S2 T +H 2 H2 T +T 2 T2 T, Ω 2 = H 2 +H T S T + T T, Ω 2 = H T + S T T T + T 2, Ω 25 = 0, Ω 2 = 0, Ω 27 = 0, Ω 28 = 0, Ω 29 = R2 T, Ω = H H T Q, Ω = H T T, Ω 5 = 0, Ω = 0, Ω 7 = 0, Ω 8 = 0, Ω 9 = 0, Ω = Q T T T, Ω 5 = 0, Ω = 0, Ω 7 = 0, Ω 8 = 0, Ω 9 = 0, Ω 55 = Q 2 Φ 2 Φ T 2 +Γ 2 +Γ T 2 +Θ 2 +Θ T 2, Ω 5 = Γ 2 +Γ Φ T +Θ T, Ω 57 = Φ T + Γ T + Θ T Θ 2, Ω 58 = 0, Ω 59 = 0, Ω = Q Γ Γ T, Ω 7 = Γ T Θ, Ω 8 = 0, Ω 9 = 0, Ω 77 = Q Θ Θ T, Ω 78 = 0, Ω 79 = 0, Ω 88 = R 1 R1 T, Ω 89 = 0, Ω 99 = (R 2 Σ 1 ) (R 2 Σ 1 ) T, Ā = [(A T P D T Y T ) 0 0 W1 T P W2 T P ] T, Ā 1 = [(ρ 1 (A I) T P ρ 1 D T Y T ) 0 0 ρ 1 W1 T P ρ 1 W2 T P ] T, Ā 2 = [(ρ 2 (A I) T P ρ 2 D T Y T ) 0 0 ρ 2 W1 T P 2 W2 T P ] T, Ā = [(ρ (A I) T P ρ D T Y T ) (10) 0 0 ρ W1 T P ρ W2 T P ] T, Ā = 0 0 ρ W T 1 P ρ W T 2 P ] T, C = [(C T P E T Y T [(ρ (A I) T P ρ D T Y T ) Ch T P ET h Y T ) 0 0] T, C 1 = [ρ 1 (C T P E T Y T ) C 2 = [ρ 2 (C T P E T Y T ) 0 0 ρ 2 (Ch T P ET h Y T ) 0 0 ρ (Ch T P ET h Y T ) 0 0] T, C = [ρ (C T P E T Y T ) 0 0 ρ 1 (Ch T P ET h Y T ) ] T, 0 0] T, C = [ρ (C T P E T Y T ) 0 0 ρ (Ch T P ET h Y T ) 0 0] T, S = [S1 T S2 T S T S T 0 0 l 0 0 0] T, H = [ H1 T H2 T H T H T ] T, T = [ T1 T T2 T T T T T ] T, Γ = [ Γ T Γ T 2 Γ T Γ T 0 0 ] T, Φ = [ Φ T Φ T 2 Φ T Φ T 0 0 ] T, Θ = [ Θ T Θ T 2 Θ T Θ T 0 0 ] T, in which τ Mn = τ M τ m, h Mn = h M h m. Then the system described by (1) is globally stochastically asymptotically robustly stable in the mean square. L = P 1 Y. In this case, a desired the estimator gain matrix L is given as
5 ICIC EXPRESS LETTERS, VOL., NO., Numerical Example. Consider the discrete time recurrent neural network (1) with parameters [ as follows ] [ ] [ ] [ ] A =, W =, W =, C =, C h = [ ] [ ] , Σ =, ρ = ρ 2 = ρ = ρ = 0.1. Take the activation function as g(x) = 1/2( x x 1 ). The stochastic process {ω(k)} satisfies () with δ = 0.. In this example, we assume the activation functions satisfy Assumption 2.1 with α 1 = 0.52, [ α 2 = ] [ For the network ] output, [ the parameter ] D, E and E h is given as. D =, E =, E h =. Using the Matlab LMI Control Toolbox to solve the LMI (10) for all interval time-varying delays satisfying τ(k) = + sin(kπ/2)(i.e. the lower bound τ m = 2 and the upper bound τ M = ) and h(k) = 2 + sin(kπ/2) (i.e. the lower bound h m = 1 and the upper bound h M = ), the feasible [ solution is sought ] as [ ] [ ] P = = =, [ ] [ ] [ ] Q = = =, [ ] [ ] [ ] Q =, R =, R =, [ ] Y =. Therefore, by Theorem.1 the state estimation problem is solvable, and a desired estimator gain is given by L = P 1 Y as L = [ Conclusions. In this study, we investigate the problem of state estimation for discrete stochastic recurrent neural network with interval time-delays. A sufficient condition for solvability of this problem, which takes into account the interval time-delays, has been derived. The exponential state estimator is designed to estimate the neuron states and the dynamics of estimation error is globally exponentially stable. Finally, a numerical example has been presented to demonstrate the effectiveness of the proposed approach. ]. REFERENCES [1] C. Y. Lu, T. J. Su, Y. H. Su and S. C. Huang, A delay-dependent approach to stability for static recurrent neural networks with mixed time-varying delays, Int. J. Innovative Computing, Information & Control, vol.52, pp , [2] P. Balasubramaniam and R. Rakkiyappan, Global asymptotic stability of stochastic recurrent neural networks with multiple discrete delays and unbounded distributed delays, Applied Mathematics and Computation, vol.20, pp.80-8, [] Q. Zhang, X. Wei and J. Xu, A generalized LMI-based approach to the global asymptotic stability of discrete-time delayed recurrent neural networks, Int. J. Innovative Computing, Information & Control, vol., pp , [] Y. Chen and W. Su, New robust stability of cellular neural networks with time-varying discrete and distributed delays, Int. J. Innovative Computing, Information & Control, vol., pp , [5] Y. Guo, New results on input-to-state convergence for recurrent neural networks with variable inputs, Nonlinear Analysis: Real World Applications, vol.9, pp , [] Y. Lv, W. Lv and J. Sun, Convergence dynamics of stochastic reaction diffusion recurrent neural networks with continuously distributed delays, Nonlinear Analysis: Real World Applications, vol.9, pp , [7] J. Yu, K. Zhang, S. Fei and T. Li, Simplified exponential stability analysis for recurrent neural networks with discrete and distributed time-varying delays, Applied Mathematics and Computation, vol.205, pp.5-7, 2008.
6 70 C.-W. LIAO, C.-Y. LU, K.-Y. ZHENG AND C.-C. TING [8] Q. Song, Exponential stability of recurrent neural networks with both time-varying delays and general activation functions via LMI approach, Neurocomputing, vol.71, pp , [9] R. Rakkiyappan and P. Balasubramaniam, Delay-dependent asymptotic stability for stochastic delayed recurrent neural networks with time varying delays, Applied Mathematics and Computation, vol.198, pp.52-5, [10] Q. Song, Novel criteria for global exponential periodicity and stability of recurrent neural networks with time-varying delays, Chaos, Solitons & Fractals, vol., pp , [11] L. Wang, Z. Zhang and Y. Wang, Stochastic exponential stability of the delayed reaction diffusion recurrent neural networks with Markovian jumping parameters, Physics Letters A, vol.72, pp , [12] Z. Wang, Ho, D. W. C. and X. Liu, State estimation for delayed neural networks, IEEE Trans. Neural Networks, vol.1, pp , [1] H. Huang, G. Feng and J. Cao, An LMI approach to delay-dependent state estimation for delayed neural networks, Neurocomputing, vol.71, pp , [1] Y. He. Wang, M. Wu and C. Lin, Delay-dependent state estimation for delayed neural networks, IEEE Trans. Neural Networks, vol.17, pp , 200. [15] T. Li and S. Fei, Exponential state estimation for recurrent neural networks with distributed delays, Neurocomputing, vol.71, pp.28-8, [1] T. Li, S. Fei and Q. Zhu, Design of exponential state estimator for neural networks with distributed delays, Nonlinear Analysis: Real World Applications, vol.10, pp , [17] Y. Liu, Z. Wang and X. Liu, Design of exponential state estimators for neural networks with mixed time delays, Physics Letters A, vol., pp.01-12, [18] Z. Wang, Y. Liu and X. Liu, State estimation for jumping recurrent neural networks with discrete and distributed delays, Neural Networks, vol.22, pp.1-8, [19] J. H. Park, O. M. Kwon and S. M. Lee, State estimation for neural networks of neutral-type with interval time-varying delays, Applied Mathematics and Computation, vol.20, pp , [20] S. Boyd, L. G. Ei, E. Feron and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, Philadelphia, PA: SIAM, 199. [21] C. Y. Lu, A delay-range-dependent approach to design state estimator for discrete-time recurrent neural networks with interval time-varying delay, IEEE Trans Circuits and Systems II: Express Briefs, vol.55, pp
Correspondence should be addressed to Chien-Yu Lu,
Hindawi Publishing Corporation Discrete Dynamics in Nature and Society Volume 2009, Article ID 43015, 14 pages doi:10.1155/2009/43015 Research Article Delay-Range-Dependent Global Robust Passivity Analysis
More informationResearch Article Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with Time-Varying Delays
Discrete Dynamics in Nature and Society Volume 2008, Article ID 421614, 14 pages doi:10.1155/2008/421614 Research Article Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with
More informationImproved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters
Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters Ji Yan( 籍艳 ) and Cui Bao-Tong( 崔宝同 ) School of Communication and
More informationAdaptive synchronization of chaotic neural networks with time delays via delayed feedback control
2017 º 12 È 31 4 ½ Dec. 2017 Communication on Applied Mathematics and Computation Vol.31 No.4 DOI 10.3969/j.issn.1006-6330.2017.04.002 Adaptive synchronization of chaotic neural networks with time delays
More informationResults on stability of linear systems with time varying delay
IET Control Theory & Applications Brief Paper Results on stability of linear systems with time varying delay ISSN 75-8644 Received on 8th June 206 Revised st September 206 Accepted on 20th September 206
More informationResearch Article Delay-Range-Dependent Stability Criteria for Takagi-Sugeno Fuzzy Systems with Fast Time-Varying Delays
Journal of Applied Mathematics Volume 2012rticle ID 475728, 20 pages doi:10.1155/2012/475728 Research Article Delay-Range-Dependent Stability Criteria for Takagi-Sugeno Fuzzy Systems with Fast Time-Varying
More informationLinear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays
Chin. Phys. B Vol. 21, No. 4 (212 4842 Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays P. Balasubramaniam a, M. Kalpana a, and R.
More informationAnalysis of stability for impulsive stochastic fuzzy Cohen-Grossberg neural networks with mixed delays
Analysis of stability for impulsive stochastic fuzzy Cohen-Grossberg neural networks with mixed delays Qianhong Zhang Guizhou University of Finance and Economics Guizhou Key Laboratory of Economics System
More informationDelay-Dependent Exponential Stability of Linear Systems with Fast Time-Varying Delay
International Mathematical Forum, 4, 2009, no. 39, 1939-1947 Delay-Dependent Exponential Stability of Linear Systems with Fast Time-Varying Delay Le Van Hien Department of Mathematics Hanoi National University
More informationLinear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay
Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay S. Lakshmanan and P. Balasubramaniam Department of Mathematics, Gandhigram Rural University,
More informationH Synchronization of Chaotic Systems via Delayed Feedback Control
International Journal of Automation and Computing 7(2), May 21, 23-235 DOI: 1.17/s11633-1-23-4 H Synchronization of Chaotic Systems via Delayed Feedback Control Li Sheng 1, 2 Hui-Zhong Yang 1 1 Institute
More informationRECENTLY, many artificial neural networks especially
502 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 54, NO. 6, JUNE 2007 Robust Adaptive Control of Unknown Modified Cohen Grossberg Neural Netwks With Delays Wenwu Yu, Student Member,
More informationDelay-dependent Stability Analysis for Markovian Jump Systems with Interval Time-varying-delays
International Journal of Automation and Computing 7(2), May 2010, 224-229 DOI: 10.1007/s11633-010-0224-2 Delay-dependent Stability Analysis for Markovian Jump Systems with Interval Time-varying-delays
More informationNew Stability Criteria for Recurrent Neural Networks with a Time-varying Delay
International Journal of Automation and Computing 8(1), February 2011, 128-133 DOI: 10.1007/s11633-010-0564-y New Stability Criteria for Recurrent Neural Networks with a Time-varying Delay Hong-Bing Zeng
More informationA new robust delay-dependent stability criterion for a class of uncertain systems with delay
A new robust delay-dependent stability criterion for a class of uncertain systems with delay Fei Hao Long Wang and Tianguang Chu Abstract A new robust delay-dependent stability criterion for a class of
More informationOn Computing the Worst-case Performance of Lur'e Systems with Uncertain Time-invariant Delays
Article On Computing the Worst-case Performance of Lur'e Systems with Uncertain Time-invariant Delays Thapana Nampradit and David Banjerdpongchai* Department of Electrical Engineering, Faculty of Engineering,
More informationSTABILITY ANALYSIS FOR SYSTEMS WITH LARGE DELAY PERIOD: A SWITCHING METHOD. Received March 2011; revised July 2011
International Journal of Innovative Computing, Information and Control ICIC International c 2012 ISSN 1349-4198 Volume 8, Number 6, June 2012 pp. 4235 4247 STABILITY ANALYSIS FOR SYSTEMS WITH LARGE DELAY
More informationOn Separation Principle for a Class of Networked Control Systems
On Separation Principle for a Class of Networked Control Systems Dongxiao Wu Jun Wu and Sheng Chen Abstract In this contribution we investigate a class of observer-based discrete-time networked control
More informationResearch Article Finite-Time Robust Stabilization for Stochastic Neural Networks
Abstract and Applied Analysis Volume 212, Article ID 231349, 15 pages doi:1.1155/212/231349 Research Article Finite-Time Robust Stabilization for Stochastic Neural Networks Weixiong Jin, 1 Xiaoyang Liu,
More informationOn Design of Reduced-Order H Filters for Discrete-Time Systems from Incomplete Measurements
Proceedings of the 47th IEEE Conference on Decision and Control Cancun, Mexico, Dec. 9-11, 2008 On Design of Reduced-Order H Filters for Discrete-Time Systems from Incomplete Measurements Shaosheng Zhou
More informationTime-delay feedback control in a delayed dynamical chaos system and its applications
Time-delay feedback control in a delayed dynamical chaos system and its applications Ye Zhi-Yong( ), Yang Guang( ), and Deng Cun-Bing( ) School of Mathematics and Physics, Chongqing University of Technology,
More informationSTABILIZATION OF LINEAR SYSTEMS VIA DELAYED STATE FEEDBACK CONTROLLER. El-Kébir Boukas. N. K. M Sirdi. Received December 2007; accepted February 2008
ICIC Express Letters ICIC International c 28 ISSN 1881-83X Volume 2, Number 1, March 28 pp. 1 6 STABILIZATION OF LINEAR SYSTEMS VIA DELAYED STATE FEEDBACK CONTROLLER El-Kébir Boukas Department of Mechanical
More informationTakagi Sugeno Fuzzy Sliding Mode Controller Design for a Class of Nonlinear System
Australian Journal of Basic and Applied Sciences, 7(7): 395-400, 2013 ISSN 1991-8178 Takagi Sugeno Fuzzy Sliding Mode Controller Design for a Class of Nonlinear System 1 Budiman Azzali Basir, 2 Mohammad
More informationDelay-Dependent Stability Criteria for Linear Systems with Multiple Time Delays
Delay-Dependent Stability Criteria for Linear Systems with Multiple Time Delays Yong He, Min Wu, Jin-Hua She Abstract This paper deals with the problem of the delay-dependent stability of linear systems
More informationCONSTRAINED MODEL PREDICTIVE CONTROL ON CONVEX POLYHEDRON STOCHASTIC LINEAR PARAMETER VARYING SYSTEMS. Received October 2012; revised February 2013
International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 10, October 2013 pp 4193 4204 CONSTRAINED MODEL PREDICTIVE CONTROL ON CONVEX
More informationEXISTENCE AND EXPONENTIAL STABILITY OF ANTI-PERIODIC SOLUTIONS IN CELLULAR NEURAL NETWORKS WITH TIME-VARYING DELAYS AND IMPULSIVE EFFECTS
Electronic Journal of Differential Equations, Vol. 2016 2016, No. 02, pp. 1 14. ISSN: 1072-6691. URL: http://ejde.math.txstate.edu or http://ejde.math.unt.edu ftp ejde.math.txstate.edu EXISTENCE AND EXPONENTIAL
More informationCONVERGENCE BEHAVIOUR OF SOLUTIONS TO DELAY CELLULAR NEURAL NETWORKS WITH NON-PERIODIC COEFFICIENTS
Electronic Journal of Differential Equations, Vol. 2007(2007), No. 46, pp. 1 7. ISSN: 1072-6691. URL: http://ejde.math.txstate.edu or http://ejde.math.unt.edu ftp ejde.math.txstate.edu (login: ftp) CONVERGENCE
More informationFinite-time hybrid synchronization of time-delay hyperchaotic Lorenz system
ISSN 1746-7659 England UK Journal of Information and Computing Science Vol. 10 No. 4 2015 pp. 265-270 Finite-time hybrid synchronization of time-delay hyperchaotic Lorenz system Haijuan Chen 1 * Rui Chen
More informationResearch Article Stabilization Analysis and Synthesis of Discrete-Time Descriptor Markov Jump Systems with Partially Unknown Transition Probabilities
Research Journal of Applied Sciences, Engineering and Technology 7(4): 728-734, 214 DOI:1.1926/rjaset.7.39 ISSN: 24-7459; e-issn: 24-7467 214 Maxwell Scientific Publication Corp. Submitted: February 25,
More informationLINEAR QUADRATIC OPTIMAL CONTROL BASED ON DYNAMIC COMPENSATION. Received October 2010; revised March 2011
International Journal of Innovative Computing, Information and Control ICIC International c 22 ISSN 349-498 Volume 8, Number 5(B), May 22 pp. 3743 3754 LINEAR QUADRATIC OPTIMAL CONTROL BASED ON DYNAMIC
More informationA Generalization of Some Lag Synchronization of System with Parabolic Partial Differential Equation
American Journal of Theoretical and Applied Statistics 2017; 6(5-1): 8-12 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.s.2017060501.12 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online)
More informationROBUST STABILITY TEST FOR UNCERTAIN DISCRETE-TIME SYSTEMS: A DESCRIPTOR SYSTEM APPROACH
Latin American Applied Research 41: 359-364(211) ROBUS SABILIY ES FOR UNCERAIN DISCREE-IME SYSEMS: A DESCRIPOR SYSEM APPROACH W. ZHANG,, H. SU, Y. LIANG, and Z. HAN Engineering raining Center, Shanghai
More informationSynchronization criteria for coupled Hopfield neural networks with time-varying delays
Synchronization criteria for coupled Hopfield neural networks with time-varying delays M.J. Park a), O.M. Kwon a), Ju H. Park b), S.M. Lee c), and E.J. Cha d) a) School of Electrical Engineering, Chungbuk
More informationSecure Communications of Chaotic Systems with Robust Performance via Fuzzy Observer-Based Design
212 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL 9, NO 1, FEBRUARY 2001 Secure Communications of Chaotic Systems with Robust Performance via Fuzzy Observer-Based Design Kuang-Yow Lian, Chian-Song Chiu, Tung-Sheng
More informationResearch Article Input and Output Passivity of Complex Dynamical Networks with General Topology
Advances in Decision Sciences Volume 1, Article ID 89481, 19 pages doi:1.1155/1/89481 Research Article Input and Output Passivity of Complex Dynamical Networks with General Topology Jinliang Wang Chongqing
More informationPAijpam.eu DELAY-RANGE-DEPENDENT MEAN SQUARE STABILITY OF STOCHASTIC SYSTEMS WITH INTERVAL TIME-VARYING DELAYS
International Journal of Pure and Applied Mathematics Volume 94 No. 4 2014, 489-499 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu doi: http://dx.doi.org/10.12732/ijpam.v94i4.4
More informationDeakin Research Online
Deakin Research Online This is the published version: Phat, V. N. and Trinh, H. 1, Exponential stabilization of neural networks with various activation functions and mixed time-varying delays, IEEE transactions
More informationROBUST STATE FEEDBACK CONTROL OF UNCERTAIN POLYNOMIAL DISCRETE-TIME SYSTEMS: AN INTEGRAL ACTION APPROACH
International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 3, March 2013 pp. 1233 1244 ROBUST STATE FEEDBACK CONTROL OF UNCERTAIN POLYNOMIAL
More informationPDC-based fuzzy impulsive control design with application to biological systems: predator-prey system
PDC-based fuzzy impulsive control design with application to biological systems: predator-prey system Mohsen Mahdian *, Iman Zamani **, Mohammad Hadad Zarif * * Department of Electrical Engineering, Shahrood
More informationMean square stability of discrete-time stochastic hybrid systems with interval time-varying delays
Mean square stability of discrete-time stochastic hybrid systems with interval time-varying delays Manlika Rajchakit Department of Statistics Maejo University Chiang Mai 529 Thailand Email: manlika@mju.ac.th
More informationImproved Stability Criteria for Lurie Type Systems with Time-varying Delay
Vol. 37, No. 5 ACTA ATOMATICA SINICA May, 011 Improved Stability Criteria for Lurie Type Systems with Time-varying Delay RAMAKRISHNAN Krishnan 1 RAY Goshaidas 1 Abstract In this technical note, we present
More informationDISSIPATIVITY OF NEURAL NETWORKS WITH CONTINUOUSLY DISTRIBUTED DELAYS
Electronic Journal of Differential Equations, Vol. 26(26), No. 119, pp. 1 7. ISSN: 172-6691. URL: http://ejde.math.txstate.edu or http://ejde.math.unt.edu ftp ejde.math.txstate.edu (login: ftp) DISSIPATIVITY
More informationRobust Control of Uncertain Stochastic Recurrent Neural Networks with Time-varying Delay
Neural Processing Letters (2007 26:101 119 DOI 10.1007/s11063-007-9045-x Robust Control of Uncertain Stochastic Recurrent Neural Networks with Time-varying Delay Wenwu Yu Jinde Cao Received: 18 November
More informationLMI-based criterion for global asymptotic stability
170 Int. J. Systems, Control and Communications, Vol. 1, No. 2, 2008 LMI-based criterion for global asymptotic stability of BAM neural networks with time delays Ju H. Park Robust Control and Nonlinear
More informationEXPONENTIAL SYNCHRONIZATION OF DELAYED REACTION-DIFFUSION NEURAL NETWORKS WITH GENERAL BOUNDARY CONDITIONS
ROCKY MOUNTAIN JOURNAL OF MATHEMATICS Volume 43, Number 3, 213 EXPONENTIAL SYNCHRONIZATION OF DELAYED REACTION-DIFFUSION NEURAL NETWORKS WITH GENERAL BOUNDARY CONDITIONS PING YAN AND TENG LV ABSTRACT.
More informationControl for Markov sampled-data systems with event-driven transmitter
Cui et al Advances in Difference Equations (215) 215:343 DOI 11186/s13662-15-681-6 R E S E A R C H Open Access Control for Markov sampled-data systems with event-driven transmitter Wenxia Cui *, Lu Li
More informationThis article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution
More informationResearch Article Mathematical Model and Cluster Synchronization for a Complex Dynamical Network with Two Types of Chaotic Oscillators
Applied Mathematics Volume 212, Article ID 936, 12 pages doi:1.11/212/936 Research Article Mathematical Model and Cluster Synchronization for a Complex Dynamical Network with Two Types of Chaotic Oscillators
More informationROBUST QUANTIZED H CONTROL FOR NETWORK CONTROL SYSTEMS WITH MARKOVIAN JUMPS AND TIME DELAYS. Received December 2012; revised April 2013
International Journal of Innovative Computing, Information and Control ICIC International c 213 ISSN 1349-4198 Volume 9, Number 12, December 213 pp. 4889 492 ROBUST QUANTIZED H CONTROL FOR NETWORK CONTROL
More informationIN the past decades, neural networks have been studied
Proceedings of the International MultiConference of Engineers and Computer Scientists 18 Vol II IMECS 18 March 14-1 18 Hong Kong Mixed H-infinity and Passivity Analysis for Neural Networks with Mixed Time-Varying
More informationADAPTIVE CHAOS SYNCHRONIZATION OF UNCERTAIN HYPERCHAOTIC LORENZ AND HYPERCHAOTIC LÜ SYSTEMS
ADAPTIVE CHAOS SYNCHRONIZATION OF UNCERTAIN HYPERCHAOTIC LORENZ AND HYPERCHAOTIC LÜ SYSTEMS Sundarapandian Vaidyanathan 1 1 Research and Development Centre, Vel Tech Dr. RR & Dr. SR Technical University
More informationSynchronization of a General Delayed Complex Dynamical Network via Adaptive Feedback
Synchronization of a General Delayed Complex Dynamical Network via Adaptive Feedback Qunjiao Zhang and Junan Lu College of Mathematics and Statistics State Key Laboratory of Software Engineering Wuhan
More informationConvex Optimization Approach to Dynamic Output Feedback Control for Delay Differential Systems of Neutral Type 1,2
journal of optimization theory and applications: Vol. 127 No. 2 pp. 411 423 November 2005 ( 2005) DOI: 10.1007/s10957-005-6552-7 Convex Optimization Approach to Dynamic Output Feedback Control for Delay
More informationAnti-periodic Solutions for A Shunting Inhibitory Cellular Neural Networks with Distributed Delays and Time-varying Delays in the Leakage Terms
Anti-periodic Solutions for A Shunting Inhibitory Cellular Neural Networks with Distributed Delays and Time-varying Delays in the Leakage Terms CHANGJIN XU Guizhou University of Finance and Economics Guizhou
More informationStationary distribution and pathwise estimation of n-species mutualism system with stochastic perturbation
Available online at www.tjnsa.com J. Nonlinear Sci. Appl. 9 6), 936 93 Research Article Stationary distribution and pathwise estimation of n-species mutualism system with stochastic perturbation Weiwei
More informationA Delay-dependent Condition for the Exponential Stability of Switched Linear Systems with Time-varying Delay
A Delay-dependent Condition for the Exponential Stability of Switched Linear Systems with Time-varying Delay Kreangkri Ratchagit Department of Mathematics Faculty of Science Maejo University Chiang Mai
More informationARTIFICIAL NEURAL NETWORK WITH HYBRID TAGUCHI-GENETIC ALGORITHM FOR NONLINEAR MIMO MODEL OF MACHINING PROCESSES
International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 4, April 2013 pp. 1455 1475 ARTIFICIAL NEURAL NETWORK WITH HYBRID TAGUCHI-GENETIC
More informationPositive observers for positive interval linear discrete-time delay systems. Title. Li, P; Lam, J; Shu, Z
Title Positive observers for positive interval linear discrete-time delay systems Author(s) Li, P; Lam, J; Shu, Z Citation The 48th IEEE Conference on Decision and Control held jointly with the 28th Chinese
More informationDelay-dependent stability and stabilization of neutral time-delay systems
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL Int. J. Robust Nonlinear Control 2009; 19:1364 1375 Published online 6 October 2008 in Wiley InterScience (www.interscience.wiley.com)..1384 Delay-dependent
More informationDELAY-DEPENDENT STABILITY OF DISCRETE-TIME SYSTEMS WITH MULTIPLE DELAYS AND NONLINEARITIES. Siva Kumar Tadepalli and Venkata Krishna Rao Kandanvli
International Journal of Innovative Computing, Information and Control ICIC International c 2017 ISSN 1349-4198 Volume 13, Number 3, June 2017 pp. 891 904 DELAY-DEPENDENT STABILITY OF DISCRETE-TIME SYSTEMS
More informationIMPULSIVE CONTROL OF DISCRETE-TIME NETWORKED SYSTEMS WITH COMMUNICATION DELAYS. Shumei Mu, Tianguang Chu, and Long Wang
IMPULSIVE CONTROL OF DISCRETE-TIME NETWORKED SYSTEMS WITH COMMUNICATION DELAYS Shumei Mu Tianguang Chu and Long Wang Intelligent Control Laboratory Center for Systems and Control Department of Mechanics
More informationResearch Article Convex Polyhedron Method to Stability of Continuous Systems with Two Additive Time-Varying Delay Components
Applied Mathematics Volume 202, Article ID 689820, 3 pages doi:0.55/202/689820 Research Article Convex Polyhedron Method to Stability of Continuous Systems with Two Additive Time-Varying Delay Components
More informationIN THIS PAPER, we consider a class of continuous-time recurrent
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II: EXPRESS BRIEFS, VOL. 51, NO. 4, APRIL 2004 161 Global Output Convergence of a Class of Continuous-Time Recurrent Neural Networks With Time-Varying Thresholds
More informationRobust Gain Scheduling Synchronization Method for Quadratic Chaotic Systems With Channel Time Delay Yu Liang and Horacio J.
604 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I: REGULAR PAPERS, VOL. 56, NO. 3, MARCH 2009 Robust Gain Scheduling Synchronization Method for Quadratic Chaotic Systems With Channel Time Delay Yu Liang
More informationGeneralized projective synchronization of a class of chaotic (hyperchaotic) systems with uncertain parameters
Vol 16 No 5, May 2007 c 2007 Chin. Phys. Soc. 1009-1963/2007/16(05)/1246-06 Chinese Physics and IOP Publishing Ltd Generalized projective synchronization of a class of chaotic (hyperchaotic) systems with
More informationpth moment exponential stability of stochastic fuzzy Cohen Grossberg neural networks with discrete and distributed delays
Nonlinear Analysis: Modelling and Control Vol. 22 No. 4 531 544 ISSN 1392-5113 https://doi.org/10.15388/na.2017.4.8 pth moment exponential stability o stochastic uzzy Cohen Grossberg neural networks with
More informationFilter Design for Linear Time Delay Systems
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 49, NO. 11, NOVEMBER 2001 2839 ANewH Filter Design for Linear Time Delay Systems E. Fridman Uri Shaked, Fellow, IEEE Abstract A new delay-dependent filtering
More informationGraph and Controller Design for Disturbance Attenuation in Consensus Networks
203 3th International Conference on Control, Automation and Systems (ICCAS 203) Oct. 20-23, 203 in Kimdaejung Convention Center, Gwangju, Korea Graph and Controller Design for Disturbance Attenuation in
More informationRobust Variance Constrained Filter Design for Systems with Non-Gaussian Noises
Robust Variance Constrained Filter Design for Systems with Non-Gaussian Noises Fuwen Yang, Yongmin Li, and Xiaohui Liu Abstract- In this paper, a variance constrained filtering problem is considered for
More informationCHATTERING-FREE SMC WITH UNIDIRECTIONAL AUXILIARY SURFACES FOR NONLINEAR SYSTEM WITH STATE CONSTRAINTS. Jian Fu, Qing-Xian Wu and Ze-Hui Mao
International Journal of Innovative Computing, Information and Control ICIC International c 2013 ISSN 1349-4198 Volume 9, Number 12, December 2013 pp. 4793 4809 CHATTERING-FREE SMC WITH UNIDIRECTIONAL
More informationAn efficient observer design method for singular discrete-time systems with
An efficient observer design method for singular discrete-time systems with time-delay and nonlinearity: LMI approach Rahman Hajmohammadi, Saleh Mobayen * Department of Electrical Engineering, University
More informationSTABILIZATION FOR A CLASS OF UNCERTAIN MULTI-TIME DELAYS SYSTEM USING SLIDING MODE CONTROLLER. Received April 2010; revised August 2010
International Journal of Innovative Computing, Information and Control ICIC International c 2011 ISSN 1349-4198 Volume 7, Number 7(B), July 2011 pp. 4195 4205 STABILIZATION FOR A CLASS OF UNCERTAIN MULTI-TIME
More informationRobust stability and stabilization of nonlinear uncertain stochastic switched discrete-time systems with interval time-varying delays
Appl. Math. Inf. Sci. 6 No. 3 555-565 (212) 555 Applied Mathematics & Information Sciences An International Journal c 212 NSP Robust stability and stabilization of nonlinear uncertain stochastic switched
More informationMultiple-mode switched observer-based unknown input estimation for a class of switched systems
Multiple-mode switched observer-based unknown input estimation for a class of switched systems Yantao Chen 1, Junqi Yang 1 *, Donglei Xie 1, Wei Zhang 2 1. College of Electrical Engineering and Automation,
More informationTracking Control of a Class of Differential Inclusion Systems via Sliding Mode Technique
International Journal of Automation and Computing (3), June 24, 38-32 DOI: 7/s633-4-793-6 Tracking Control of a Class of Differential Inclusion Systems via Sliding Mode Technique Lei-Po Liu Zhu-Mu Fu Xiao-Na
More informationH State Feedback Control of Discrete-time Markov Jump Linear Systems through Linear Matrix Inequalities
H State Feedback Control of Discrete-time Markov Jump Linear Systems through Linear Matrix Inequalities A. P. C. Gonçalves, A. R. Fioravanti, M. A. Al-Radhawi, J. C. Geromel Univ. Estadual Paulista - UNESP.
More informationAcceleration of Levenberg-Marquardt method training of chaotic systems fuzzy modeling
ISSN 746-7233, England, UK World Journal of Modelling and Simulation Vol. 3 (2007) No. 4, pp. 289-298 Acceleration of Levenberg-Marquardt method training of chaotic systems fuzzy modeling Yuhui Wang, Qingxian
More informationOVER the past one decade, Takagi Sugeno (T-S) fuzzy
2838 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I: REGULAR PAPERS, VOL. 53, NO. 12, DECEMBER 2006 Discrete H 2 =H Nonlinear Controller Design Based on Fuzzy Region Concept and Takagi Sugeno Fuzzy Framework
More informationChaos suppression of uncertain gyros in a given finite time
Chin. Phys. B Vol. 1, No. 11 1 1155 Chaos suppression of uncertain gyros in a given finite time Mohammad Pourmahmood Aghababa a and Hasan Pourmahmood Aghababa bc a Electrical Engineering Department, Urmia
More informationController synthesis for positive systems under l 1-induced performance
Title Controller synthesis for positive systems under l 1-induced performance Author(s) Chen, X; Lam, J; Li, P; Shu, Z Citation The 24th Chinese Control and Decision Conference (CCDC 212), Taiyuan, China,
More informationEXPONENTIAL STABILITY OF SWITCHED LINEAR SYSTEMS WITH TIME-VARYING DELAY
Electronic Journal of Differential Equations, Vol. 2007(2007), No. 159, pp. 1 10. ISSN: 1072-6691. URL: http://ejde.math.txstate.edu or http://ejde.math.unt.edu ftp ejde.math.txstate.edu (login: ftp) EXPONENTIAL
More informationFixed-Order Robust H Filter Design for Markovian Jump Systems With Uncertain Switching Probabilities
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 4, APRIL 2006 1421 Fixed-Order Robust H Filter Design for Markovian Jump Systems With Uncertain Switching Probabilities Junlin Xiong and James Lam,
More informationIEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 21, NO. 1, JANUARY /$ IEEE
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 21, NO. 1, JANUARY 2010 11 Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays Zidong
More informationGeneralized Function Projective Lag Synchronization in Fractional-Order Chaotic Systems
Generalized Function Projective Lag Synchronization in Fractional-Order Chaotic Systems Yancheng Ma Guoan Wu and Lan Jiang denotes fractional order of drive system Abstract In this paper a new synchronization
More informationStability Analysis for Linear Systems under State Constraints
Stabilit Analsis for Linear Sstems under State Constraints Haijun Fang Abstract This paper revisits the problem of stabilit analsis for linear sstems under state constraints New and less conservative sufficient
More informationChaos Synchronization of Nonlinear Bloch Equations Based on Input-to-State Stable Control
Commun. Theor. Phys. (Beijing, China) 53 (2010) pp. 308 312 c Chinese Physical Society and IOP Publishing Ltd Vol. 53, No. 2, February 15, 2010 Chaos Synchronization of Nonlinear Bloch Equations Based
More informationRobust PID Controller Design for Nonlinear Systems
Robust PID Controller Design for Nonlinear Systems Part II Amin Salar 8700884 Final Project Nonlinear Control Course Dr H.D. Taghirad 1 About the Project In part one we discussed about auto tuning techniques
More informationResearch Article Reliability Analysis of Wireless Sensor Networks Using Markovian Model
Journal of Applied Mathematics Volume 212, Article ID 76359, 21 pages doi:1.1155/212/76359 Research Article Reliability Analysis of Wireless Sensor Networks Using Markovian Model Jin Zhu, Liang Tang, Hongsheng
More informationState Estimation for Discrete-time Markovian Jumping Neural Networks with Mixed Mode-Dependent Delays
SUBMITTED 1 State Estimation for Discrete-time Markovian Jumping Neural Networks with Mied Mode-Dependent Delays Yurong Liu, Zidong Wang and Xiaohui Liu Abstract In this paper, we investigate the state
More informationComplete Synchronization, Anti-synchronization and Hybrid Synchronization Between Two Different 4D Nonlinear Dynamical Systems
Mathematics Letters 2016; 2(5): 36-41 http://www.sciencepublishinggroup.com/j/ml doi: 10.11648/j.ml.20160205.12 Complete Synchronization, Anti-synchronization and Hybrid Synchronization Between Two Different
More informationH fault detection for a class of T-S fuzzy model-based nonlinear networked control systems
Preprints of the 9th World ongress he International Federation of Automatic ontrol H fault detection for a class of -S fuzzy model-based nonlinear networked control systems Weiguang Ding Zehui Mao Bin
More informationStability and hybrid synchronization of a time-delay financial hyperchaotic system
ISSN 76-7659 England UK Journal of Information and Computing Science Vol. No. 5 pp. 89-98 Stability and hybrid synchronization of a time-delay financial hyperchaotic system Lingling Zhang Guoliang Cai
More informationACTIVE CONTROLLER DESIGN FOR THE OUTPUT REGULATION OF THE WANG-CHEN-YUAN SYSTEM
ACTIVE CONTROLLER DESIGN FOR THE OUTPUT REGULATION OF THE WANG-CHEN-YUAN SYSTEM Sundarapandian Vaidyanathan Research and Development Centre, Vel Tech Dr. RR & Dr. SR Technical University Avadi, Chennai-600
More informationResearch on robust control of nonlinear singular systems. XuYuting,HuZhen
Advances in Engineering Research (AER), volume 107 2nd International Conference on Materials Engineering and Information Technology Applications (MEITA 2016) Research on robust control of nonlinear singular
More informationASYMPTOTIC STABILITY IN THE DISTRIBUTION OF NONLINEAR STOCHASTIC SYSTEMS WITH SEMI-MARKOVIAN SWITCHING
ANZIAM J. 49(2007), 231 241 ASYMPTOTIC STABILITY IN THE DISTRIBUTION OF NONLINEAR STOCHASTIC SYSTEMS WITH SEMI-MARKOVIAN SWITCHING ZHENTING HOU 1, HAILING DONG 1 and PENG SHI 2 (Received March 17, 2007;
More informationGlobal Chaos Synchronization of Hyperchaotic Lorenz and Hyperchaotic Chen Systems by Adaptive Control
Global Chaos Synchronization of Hyperchaotic Lorenz and Hyperchaotic Chen Systems by Adaptive Control Dr. V. Sundarapandian Professor, Research and Development Centre Vel Tech Dr. RR & Dr. SR Technical
More informationRobust set stabilization of Boolean control networks with impulsive effects
Nonlinear Analysis: Modelling and Control, Vol. 23, No. 4, 553 567 ISSN 1392-5113 https://doi.org/10.15388/na.2018.4.6 Robust set stabilization of Boolean control networks with impulsive effects Xiaojing
More informationGLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS BY ACTIVE NONLINEAR CONTROL
GLOBAL CHAOS SYNCHRONIZATION OF HYPERCHAOTIC QI AND HYPERCHAOTIC JHA SYSTEMS BY ACTIVE NONLINEAR CONTROL Sundarapandian Vaidyanathan 1 1 Research and Development Centre, Vel Tech Dr. RR & Dr. SR Technical
More informationOn Dwell Time Minimization for Switched Delay Systems: Free-Weighting Matrices Method
On Dwell Time Minimization for Switched Delay Systems: Free-Weighting Matrices Method Ahmet Taha Koru Akın Delibaşı and Hitay Özbay Abstract In this paper we present a quasi-convex minimization method
More informationConsensus of Multi-Agent Systems with
Consensus of Multi-Agent Systems with 1 General Linear and Lipschitz Nonlinear Dynamics Using Distributed Adaptive Protocols arxiv:1109.3799v1 [cs.sy] 17 Sep 2011 Zhongkui Li, Wei Ren, Member, IEEE, Xiangdong
More information