A Cognitive Neural Linearization Model Design for Temperature Measurement System based on Optimization Algorithm

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for Temperture Mesurement System bsed on Optimiztion Algorithm Dr. Mechnicl Engineering Deprtment, University of Technology, Bghdd e-mil: hyder_bed2002@yhoo.

1 for Temperture Mesurement System bsed on Optimiztion Algorithm Dr. Mechnicl Engineering Deprtment, University of Technology, Bghdd e-mil: Received: 29/12/2014 Accepted: 19/5/2015 Abstrct The min core of this pper is to design n experimentl method for estimting of the nonlinerity, clibrting nd testing of the different types of thermocouples temperture sensors (J, K, T, S nd R) using multi-lyer perceptron (MLP) neurl network bsed on slice genetic (SG) optimiztion lerning lgorithm. Temperture sensor hs nonlinerity behvior nture in its output response but it requires liner behvior output with ccepts pproximtion in ccurcy level, noise nd mesurement errors. Therefore, neurl network topology is proposed with five min steps lgorithm to reduce the effected noise nd minimize the mesured errors. Mtlb simultion results nd lbortory work (LbVIEW) vlidte the preciously of the proposed cognitive neurl lineriztion lgorithm in terms of clculting the temperture from the different types of thermocouples temperture sensors nd minimizing the error between the ctul temperture output nd neurl lineriztion temperture output s well s overcoming the problem of the over lerning in the lineriztion model with the minimum number of fitness evlution for the lerning lgorithm... Keywords: Thermocouple Temperture Sensors, Neurl Network Topology, Slice Genetic Algorithm, Mtlb, LbVIEW. 61

2 1. Introduction Temperture sensors re essentil elements in mny of the industril pplictions in order to monitor nd control the temperture [1]. In generl, sensors tke certin form of input (temperture, pressure, ltitude, etc.) nd convert it, through red-out circuitry, into redings tht cn be interpreted. However, mny types of sensors re nonliner in nture from which liner output is desired. There re mny different sensors for temperture mesurement such s thermocouple types (J, K, T, S nd R) which re the most commonly used [2]. They re preferred in industril pplictions due to their low cost, wide opertion rnge nd fst response time. Thermocouples lso hve nonliner outputs relted to temperture. Therefore, the proposed cognitive neurl network lineriztion technique is necessry in order to solve the problem of linerizing sensor. On the other hnd, the nlog circuits re frequently used for improving the linerity of the sensor chrcteristics, which implies dditionl nlog hrdwre nd typicl problems prticulr to nlog circuits such s temperture drift, gin nd offset error [3]. In recent yers, ppliction of rtificil neurl networks (ANNs) hs emerged s promising re of reserch in the field of instrumenttion nd mesurement such s explined in [4], [5] nd [6] becuse whenever the system hs reltion-ship between the input nd output, nd these reltion-ships hve cpbility for lerning, re-plnning, re-ssembly, reorigintion bsed neurl networks this system cn be clled cognitive neurl network [7]. The contribution of this pper is precision lineriztion temperture system bsed on the cognitive neurl network topology with slice genetic lerning lgorithm used s fst nd stble optimiztion technique. The following section contins the description of the temperture mesurement system bsed Ntionl Instrument Dt Acquisition. Section three, derives the proposed cognitive neurl network lineriztion topology with slice genetic lgorithm. Section four, shows the simultion results nd lbortory work of the proposed cognitive neurl lineriztion lgorithm nd the lst section explins the conclusions of the reserch. 2. Temperture Mesurement System Thermocouples re the most populr temperture sensors used in pplictions becuse there re mny dvntges such s chep, stndrd connectors, mesure wide rnge of tempertures, self-powered, requiring no externl power supply, extremely rugged, not frgile, nd cn withstnd hrsh environments over other types of temperture sensors such s RTDs nd thermistors [8]. A thermocouple genertes voltge proportionl to the mesurement junction temperture t mv levels while the cold junction temperture must be known nd constnt in order to mke n ccurte mesurement [3]. The block digrm of the temperture mesurement system is shown in Fig. 1 bsed on dt cquisition system from Ntionl Instrument (NI) with LbVIEW progrmming [9] nd [10]. 62

J-type thermocouple K-type thermocouple T-type thermocouple S-type thermocouple R-type thermocouple 5B37-J 5B37-K 5B37-T 5B37-S 5B37-R G P I B Figure 1.

Generl purpose interfce bus GPIB. Figure 2 Thermocouple signl conditioner 5B37 module [11].

module temperture rnge nd the 5B37 (G). So it cn be used the following eqution (1) [11]. Vout ( VTC VZero ) G (1) where: V out : is the 5B37 type (J, K, T, S nd R) module output from (0 to 5Volt).

3 J-type thermocouple K-type thermocouple T-type thermocouple S-type thermocouple R-type thermocouple 5B37-J 5B37-K 5B37-T 5B37-S 5B37-R G P I B Figure 1. Temperture mesurement system It consists of the following: Five different thermocouple types (J, K, T, S nd R). Thermocouple signl conditioner 5B37 module, s shown in Fig. 2. Generl purpose interfce bus GPIB. Figure 2 Thermocouple signl conditioner 5B37 module [11]. To determine the output voltge from 5B37 module, three prmeters must be known: the thermocouple input voltge t mesurement temperture, the thermocouple input signl t the minimum point of the 5B37 module temperture rnge nd the 5B37 (G). So it cn be used the following eqution (1) [11]. Vout ( VTC VZero ) G (1) where: V out : is the 5B37 type (J, K, T, S nd R) module output from (0 to 5Volt). V TC : is the thermocouple output voltge in (mv) t the temperture being mesured. Vzero : is the thermocouple output voltge in (mv) t the minimum temperture spn specified for the (5B37- J, 5B37-K, 5B37-T, 5B37-S nd 5B37-R) re equl to (-4.632, , , 0 nd 0) mvolt, respectively. Gin (G) : is the throughput gin in (V/mV) of the (5B37-J, 5B37-K, 5B37-T, 5B37-S nd 5B37-R) modules re equl to (0.105, 0.087, 0.206, nd 0.239) respectively. The I/O interfce crd uses GPIB s shown in Fig. 3 nd connects with Lptop computer. To mesure the thermocouple output voltge in (mv), it is used eqution (2) in order to find the lerning nd testing dt sets for the linerized neurl model bsed on the polynomil form. Vout VTC VZero (2) G 63

Figure 3 The GPIB crd with thermocouple modules. The polynomil is in the following form [6]: 2 n T 0 1v 2 v... nv (3) where v : is the thermocouple voltge in volts.

4 Figure 3 The GPIB crd with thermocouple modules. The polynomil is in the following form [6]: 2 n T 0 1v 2 v... nv (3) where v : is the thermocouple voltge in volts. T : is the temperture in degrees Celsius, n : re coefficients for ech thermocouple type. The Ntionl Institute of Stndrds nd Technology (NIST) polynomil coefficients for severl populr thermocouple types over selected rnge of temperture with errors re listed in Tble 1 [6]. 64

5 IJCCCE Vol.15, No.1, Cognitive Neurl Network Lineriztion Topology The cognitive neurl lineriztion lgorithm for the temperture mesurement system cn be described s the proposed five steps lgorithm bsed on multi-lyered neurl networks, s shown in Fig. 4. Input- Output Ptterns Neurl Network Model Structure Model Vlidtion Not Accept Model: Accept Lerning Algorithm nd Model Estimtio 3.1. The Input-Output Ptterns Dynmics Model Represent -tion Figure 4 Five steps of cognitive neurl Lineriztion lgorithm. The trining neurl networks often required the existence of set of input nd output ptterns clled the trining set nd this kind of lerning is clled supervised lerning [12] nd [13] Neurl Networks Model Structure This section focuses on the neurl lineriztion model using the multi-lyer perceptron (MLP) neurl network structure, s shown in Fig. 5, which consists from the nodes of input lyer, hidden lyer nd output lyer [14]. The network nottions re s follows: V : Weight mtrix of the hidden lyers. n Vb : Weight vector of the hidden lyers. W : Weight mtrix of the output lyer. b Wb b : Weight vector of the output lyer. To explin these clcultions, consider the generl th neuron in the hidden lyer shown in Fig.5. The inputs to this neuron consist of n n dimensionl vector nd (n th is the number of the input nodes). Ech of these inputs hs weight V ssocited with it. The first clcultion within the neuron consists of clculting the weighted sum net of the inputs s in (4) [14]: net nh V 1 n Z n bis Vb where nh : is number of the hidden nodes. h (4) Z n Input Lyer. V n Hidden Lyer H. H. H W b L Thermocouple J Thermocouple K Thermocouple T Thermocouple S Thermocouple R Vb Wb b bis bis Figure 5 The multi-lyer perceptron neurl networks ct s cognitive lineriztion model. 65

6 Next the output of the neuron h is clculted s the continuous sigmoid function of the net s in (5): h = H( net ) (5) 2 H( net ) = 1 net e 1 (6) Once the outputs of the hidden lyer re clculted, they re pssed to the output lyer. In the output lyer, five liner neurons re used to clculte the weighted sum (net o ) of its inputs (the output of the hidden lyer s indicted in (7)). nh neto b = Wb h bis Wbb 1 (7) where W : is the weight between the hidden b neuron h nd the output neuron. The five liner neurons, then, psses the sum (neto b ) through liner function of slope 1 (nother slope cn be used to scle the output) s: O ( ) b L netcb (8) The outputs of the neurl network lineriztion model represent the temperture of thermocouple types (J, K, T, S nd R) Lerning Algorithm nd Model Estimtion In this work, it is hoped to improve the convergence chrcteristics nd the response ccurcy by reducing the processing time of the lerning lgorithm thus, the SGA will be employed to lern the weights of the neurl network lineriztion model. The SGA is n improved form of the clssic GA, it hs sme evolutionry opertors, i.e., crossover nd muttion which re the most importnt prts responsible for the performnce influencing. The min cores of SGA re dividing the popultion into slices nd duplicting good individuls. The opertion of dividing will led to implementing the optimiztion in multi dimensions this will speed up the process of optimiztion while the process of dupliction will give high opportunity to good individuls to exhibit ll the best trits especilly when pplying rndom crossover to it [15] nd [16]. In this work, it is stisfctory to tke six slices thus; dimensions of ech slice cn be clculted s (9). (popultion size) Dimn [ m] ( Vn Vb Wb Wbb ) 6 (9) The men squre error function is criterion for estimting the lineriztion model performnce for temperture mesurement system s in (10). N J [( TeJ ) ( TeK ) ( TeT ) ( TeS ) ( TeR ) N k 1 (10) Since the SGA mximizes its fitness function, it is necessry to mp the objective function (MSE) to the fitness function by using (11) [17]. 1 fitness (11) objectivefunction where is constnt > 0 chosen to void division by zero. The lerning steps of the neurl lineriztion model prmeters by using SGA cn be described in detil s follows [15] nd [16]: Step 1: Initilize rndomly six slices with dimension [10 181], i.e. the proposed popultion size (number of individuls) will equl to 60. Step 2: Clculte the fitness of ech individul in ech slice. Step 3: For ech slice verticlly find the globl mximum fitness by using (11). 66

7 Step 4: Horizontlly find the optiml solution for ll slices the first slices optiml individul will be got now. Step 5: Duplicte the individul horizontlly, which sponsors with horizontl mximum fitness. Step 6: Mke selection s in Clssic GA. Step 7: Apply rithmetic rndom crossover with proposed crossover probbility 0.85 (this will let the duplicted individuls produce their best). Step 8: Apply muttion s in Clssic GA with proposed muttion probbility of Step 9: Clculte the fitness verticlly then find the slices for globl mximum fitness. Step 10: Horizontlly find the optiml solution for ll slices. Step 11: Find the optiml globl by compring step 11 to step 4. Step 12: Compre individul s fitness in the current genertion, with the previous one, nd then pick out the best individuls to crete the new popultion. Step 13: Repet Steps (6 to 12) until the stopping criterion is stisfied Dynmics Model Representtion The dynmic model of the proposed cognitive neurl lineriztion model for the temperture mesurement system cn be used for prediction in one-step configurtion. Prediction mens tht the neurl networks model nd the ctul system model receive the sme externl inputs, but the output of the ctul system feed to the neurl network model in order to ffect the dynmic behvior of the neurl networks model by the ctul system model nd the model predicts one step into future. The one-step prediction configurtion is clled series-prllel model is shown in Fig. 6 [13] nd [14]. Inputs Voltge System Equtions Temperture outputs 1 Z + Lineriztion Model Error n 1 Z Cognitive Neurl Lineriztion Temperture Mesurement System - Neurl Networks Outputs n 1 Z Slice Genetic Algorithm Figure 6 The series-prllel structure model Model Vlidtion The end tsk of lineriztion model for the temperture mesurement system is the vlidtion of the cognitive neurl network model qulity. The vlidtion is to check the model qulity by using nother dt set which clled testing dt set. The test dt set should excite the system nd the neurl model. The vlidtion process is performed using two 67

different pproches; the first is the prediction error between the ctul output of the system nd neurl network model output. The second vlidtion cn be chieved through visuliztion of the prediction.

8 different pproches; the first is the prediction error between the ctul output of the system nd neurl network model output. The second vlidtion cn be chieved through visuliztion of the prediction. This visuliztion is given s grphic representtion of the ctul outputs nd the predictions clculted by the neurl network model. 4. Simultion Results nd Lbortory Work The cognitive neurl lineriztion model which is proposed in section 3 for the temperture mesurement system which is described in section 2 is verified by mens of the LbVIEW pckge, the virtul instrument development pltform by Ntionl Instrument. LbVIEW is grphicl progrmming lnguge using icon code insted of text progrmming lnguge therefore, it s simple, intuitive nd the most widely pplied in the mesurement system [9] nd [10]. The first step in the cognitive neurl lineriztion model lgorithm ws ttended the lerning nd testing thermocouple voltge sets from the mesurement system, s shown in Fig. 1, for five thermocouples sensors types (J, K, T, S nd R) with different tempertures within rnge 25 C o to 250 C o, s shown in Fig. 7 nd 8 for lerning nd testing sets respectively. The inputs lerning nd testing ptterns of the temperture re from (25 to 250)C o nd the output voltge of the thermocouples modules types (5B37-J, K, T, S nd R) nd the thermocouples outputs voltges types (J, K, T, S nd R) re shown in Tble 2. The second step in the cognitive neurl lineriztion model lgorithm ws lerned the multi-lyer neurl network which consists of the nodes of input lyer, hidden lyer nd output lyer s ( ) respectively by using the lerning steps of the slice genetic lgorithm s the third step in the proposed lgorithm which consists of six slices ech slice hs dimension [10 181] with mximum popultion size is equl to 60 nd for ech slice is equl to ten nd the lerning pttern set is equl to 100 ptterns with number of itertion is equl to 20. Tble 2. Output voltge of the thermocouples nd modules. Thermocou ple Module Type 5B37-J 5B37-K 5B37-T 5B37-S 5B37-R Output Voltge (volt) of the Thermocou ple Module Type to to to to to Thermocou ple Type J K T S Output Voltge (mv) of the Thermoc ouples to to to to to Mtlb progrmming uses to lern nd test the cognitive neurl lineriztion mode for temperture mesurement system with using slice genetic lgorithm in order to obtin better performnce nd fster convergence with the minimum number of fitness evlution. It is very necessry to normlize the input signls of Tble 2 nd Fig. 7 nd 8 between (-1 to +1). The signls entered to or emitted from the neurl network hve been normlized to lie within (-1 to +1) in order to overcome numericl problems tht is involved within rel vlues. R 68 Figure 7 Lerning set thermocouple voltge.

9 . Figure 8 Testing set thermocouple voltge. Therefore, scling functions hve to be dded t the cognitive neurl lineriztion model terminls to convert the scled vlues to ctul vlues nd vice vers in order to chieve the dynmics model representtion of the cognitive neurl lineriztion model s fourth step in the proposed lgorithm. After 20 itertions for lerning the cognitive neurl lineriztion model, the men squre error is equl to s shown in Fig. 9. Then it cn be observed tht the output temperture of the cognitive neurl lineriztion model is follow the ctul temperture mesurement for ech thermocouple, type s shown in Fig. 10. Men Squre Error (MSE) x Figure 10 Model temperture output for lerning set thermocouple voltge. Figure 11 shows the error between the desired temperture nd the output temperture of the cognitive neurl lineriztion model for lerning set for ech thermocouple type nd the mximum temperture error rnge is 3 C o from full scle t (0 to 250C o ) therefore the error in not cler in the Fig. 10. The fifth step in proposed of the cognitive neurl lineriztion model lgorithm ws tested the multi-lyer neurl network by the testing set in order to verify the model nd cler from ech of lerning problems. It cn be indicted tht the output temperture of the cognitive neurl lineriztion model of the temperture mesurement system is closely to the desired ctul temperture mesurement, s shown in Fig. 12 for ech thermocouple type Number of Itertion Figure 9 The men squre error for the lerning set. 69

10 Figure 11 Temperture model error for lerning set thermocouple voltge. Figure 13 Temperture model error for testing set thermocouple voltge. Figure 12 Model temperture output for testing set thermocouple voltge. Figure 13 shows the error between the ctul temperture nd the output temperture of the cognitive neurl lineriztion model for testing set for ech thermocouple type nd the mximum temperture error rnge is 3C o from full scle t (0 to 250C o ) therefore the error in not cler in the Fig Conclusions The cognitive neurl lineriztion model for the temperture mesurement system bsed on the proposed five steps lgorithm with slice genetic technique for lerning the multi-lyer perceptron neurl network hs been presented in this work. Lbortory work vi LbVIEW pckge nd simultion results vi Mtlb pckge demonstrte the cognitive neurl network pproch cting s preciously lineriztion model for the five different types of the thermocouples which hve nonliner behvior. The proposed lerning lgorithm for the cognitive neurl lineriztion model hs the cpbility s follows: Fst nd stble tuning weights of the neurl network with minimum number of fitness evlutions s compred with [5] nd [6]; Reducing the output oscilltion compre with [5]; Minimizing the error between the ctul temperture output nd neurl lineriztion temperture output compre with [5]; Overcoming the problem of the over lerning in the lineriztion model. 70

11 References [1] R.P. Areny nd J.G. Webster, Sensors nd Signl Conditioning. Wiley, New York, [2] L. Michlski, K. Eckersdorf, J. Kuchrski nd J. Mcghee. Temperture Mesurement. Wiley Interscience, [3] K. Dnismn, I. Dlkirn nd F.V. Celebi. Design of high precision temperture mesurement system bsed on rtificil neurl network for different thermocouple types. Mesurement, vol. 39 (2006), pp [4] Y. Chong, C. Quek nd P. Loh A novel neuro-cognitive pproch to modeling trffic control nd flow bsed on fuzzy neurl techniques. Expert Systems with Applictions, vol. 36, (2009) pp [5] N. J. Medrno, R. H. Alonso nd B. M. Brio. A Thermocouple Model Bsed on Neurl Networks. Proceedings of the 6th Interntionl Work-Conference on Artificil nd Nturl Neurl Networks: Bio-inspired Applictions of Connectionism-Prt II. (2001), pp [6] A. Al-Arji. Design of Neurl Networks Lineriztion for Temperture Mesurement System Bsed on Different Thermocouples Sensors Types. Eng. nd Tech. Journl. Vol. 27, no. 8, (2009), pp [7] A. Al-Arji, Design of cognitive neurl predictive controller for mobile robot, PhD thesis, Brunel University, United Kingdom, (2012). [8] Y. Lei, Z. Xu, L. Zhng nd L. Meng. An environmentlly-flexible mesurement system for mmunition temperture detection in the powder chmber with high ccurcy. Mesurement, vol. 45, (2012), pp [9] J. Song. Air condition Control system of Weving Workshop Bsed On LbVIEW. Physics Procedi, vol. 24, (2012), pp [10] Z. Mingle, Y. Jintin, J. Guogung nd L. Gng. System on Temperture Control of Hollow Fiber Spinning Mchine Bsed on LbVIEW. Procedi Engineering, vol. 29 (2012), pp [11] Text mnul from nlog device compny. Accessed 1 Aug [12] Omtu, M. Khlid, nd R. Yusof, Neuro - control nd its pplictions. London: Springer-Velg, [13] Nells, "Nonliner system identifiction, Springer Verlg Berlin Heidelberg, [14] M. Zurd, Introduction to Artificil Neurl Systems (Jico Publishing House, Pws Pub Co. 1992). [15] K. E. Dgher, Design of n uto-tuning PID controller for systems bsed on slice genetic lgorithm. Irqi Journl of Computer, Communiction, Control Systems Eng. 13(3), 1-9 (2013). [16] A. Al-Arji, Development of kinemtic pthtrcking controller design for rel mobile robot vi bck-stepping slice genetic robust lgorithm technique. Arbin Journl for Science nd Engineering. Vol. 39, no. 12, (2014), pp [17] A. Al-Arji, Genetic lgorithm nd Elmn neurl network used for tuning the prmeters of the PID neurl controller bsed model reference. Irqi Journl of Computer, Communiction, Control Systems Eng. vol. 5, no.1, (2005), pp

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