High-order Fuzzy Neural Network Algorithm in the Application of Inner Mongolia Grassland Scenic Tourist Traffic Prediction Research

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1 High-order Fuzzy Neural Network Algorithm in the Application of Inner Mongolia Grassland Scenic Tourist Traffic Prediction Research Juan Zhao Department of Mathematics and Physics Hefei University Hefei Anhui, China, Abstract In Inner Mongolia grassland tourism development with the development of the western development and the constant improvement of the social economy level and progress quickly. Inner Mongolia grassland tourism development as well as improve the demand of grassland tourism highway in the region. In the process of grassland tourism highway construction, traffic forecast is the basis of the construction plan. The study of general highway traffic prediction method at home and abroad has been maturing. But the theory research of grassland tourism highway is very lack. On the basis of protecting ecological environment, combined with the characteristics of grassland tourism in Inner Mongolia, to carry out the study of grassland tourism highway traffic, help in Inner Mongolia regional tourism highway planning and construction of scientific development, attract more tourists, tourism transportation system, Mongolia region tourism industry, led growth in Inner Mongolia has important significance. In Inner Mongolia grassland tourism highway mainly embodied in the characteristics of tourist Numbers vary with the change of seasons and the larger the rapid growth of tourism peak traffic caused the tourist road congestion, in the off-season, rarely causes traffic highway traffic facilities underused. Through the comparison of the characteristics of the various prediction models, this paper use fuzzy neural network to establish the prediction model, and the methods to strengthen a bit, as the Inner Mongolia region in Inner Mongolia grassland tourism highway planning and development of the tourism industry and the construction of scientific tourism transportation system to offer help. Keywords - Inner Mongolia; grassland tourism; traffic prediction; a fuzzy neural network I. INTRODUCTION In recent years, on the one hand, with the continuous development of social economy, people pay more attention to the pursuit of material things in addition to the demand for spirit, etc., have higher requirements. On the other hand, due to the grassland culture of Inner Mongolia region unique and landscape value makes the grassland tourism more and more attention. So there will be a large number of tourists choose to Inner Mongolia grassland tourism, on the prairie people bring higher income at the same time, too many tourists also brought severe damage to the grassland ecology, in order to both grasslands residents income and grassland ecological protection at the same time, we need to forecast the tourist traffic. For the grassland passenger traffic is the most intuitive reaction of grassland tourism highway, so in this article, we will focus on analysis of traffic, and further improve the tourism road service level. According to traffic prediction problem, there are a lot of solutions. Tourism affected by seasonal factors, especially the holidays affected, inefficiency of using traditional time series prediction (Chen Zhe, Feng Tianjin, Zhang Haiyan, 2001) [1]. Using Markov analysis method to predict urban intersection traffic control system in each phase of the traffic flow, established by the method of simulation model results show that the algorithm is to predict the results of the flow and the error between the measured flow is small, so the short-term forecasting traffic aspects of the method is feasible (Jiang Yaping, Guo Junliang, 2012) [2]. Will be introduced to the error feedback coefficient higher-order chaotic time series prediction, but implementation of chaos theory to real-time traffic prediction model is improved (Dong Chaojun, Liu Zhiyong, Qui Zulian, 2004) [3]. Can make use of the combination of fuzzy logic and neural network model to forecast the short-term traffic flow, and this method can get good effect (Shen Guojiang, Wang Xiaohu, Kong Xiangjie, 2011) [4]. On the basis of BP and RBF neural network, using the genetic algorithm in optimization theory and birds can get good prediction effect. (Anuja Nagare, Shalini Bhatia, 2012) [5]. Although previous research results to a great extent, to solve the problem of traffic forecast, but the established model and the method of using, there is still a lack in the aspect of prediction accuracy, in this article, we will study the high-order fuzzy neural network in the application of the problem, hope you can get better and accurate model. The rest of this article is organized as follows. The second part of Inner Mongolia grassland tourist traffic problems are analyzed in detailed description. The third part of analysis is introduced according to the proposed problem of technology in the past. The fourth part introduces in detail in this paper, the application of new technology, new method. The fifth part of this article has carried on the detailed technical innovation methods course description and the model prediction results for data analysis. The sixth part is a total of this article. DOI /IJSSST.a.16.2A ISSN: x online, print

2 II. DESCRIPTION OF THE PROBLEM A. The Basic Situation of Inner Mongolia Grassland Tourism Highway Inner Mongolia autonomous region is located in China's northern borders. Its territory has a wide distribution of grassland, and mainly distributed in the greater hinggan mountains, west of mountain, helan mountain, north of Inner Mongolia plateau and the edge of the hilly mountain and the ordos plateau, natural grassland area of million hectares, the total area of 73.26%.Is important in north China grassland resources and homeland ` green barrier (Ma Yunyun, 2007) [6]. Figure 3. China Road Map Figure 1. Inner Mongolia Grassland Scenery In order to promote the development of grassland tourism in Inner Mongolia, it is necessary to vigorously strengthen the tourism highway construction. By road than by rail and air transport has more flexibility. Link to a specific traffic from the passenger flow distribution of tourist spots, realize the door to door service relies on road transport, especially in grassland areas, as tourists travel highway transportation mode choice. At the same time through the figure 2 can be clearer to see in Inner Mongolia highway passenger quantity proportion rises year by year, namely the effect of highway transportation is more and more big. By figure 3 national highway profile can be seen that distribution of highway in Inner Mongolia is not fully developed. Figure 2. Inner Mongolia Highway Passenger Quantity Proportion B. Inner Mongolia Grassland Tourism Highway Characteristics In Inner Mongolia grassland tourism highway is mainly relying on the grassland resource is a special kind of road type, its characteristic in addition to containing general characteristics of tourist road, also has its particularity, namely: the vision, the landscape of the single; Entertaining; Security requirement is higher; The focus of the grassland tourism highway design consideration is different from general highway; Traffic tourist Numbers significant seasonal changes. III. STATE OF THE ART For the proposed traffic prediction problem, there have been many experts and scholars made in-depth research, in this chapter, we introduced to these methods. A. The Grey System Theory Prediction Method Grey system is the study of "partial information clear, the partial information unknown" "small sample, poor information" uncertainty system, it through to the known part of the information generated, to development, to understand and know the world now. So, in the event information collection is not complete or information without obvious regularity, consider using grey prediction method (Che Chiang Hsu, Chia Yon Chen, 2004) [7]. When using this method, adopt the method of accumulative first to deal with time data column, the data column of the randomness of the weakening, which translates into more regular data column. Such as the given data, (0) (0) (0) (0) x ( t ) i x ( t1), x ( t2), x ( tn) (1) Is the time sequence regularity is not obvious, do data accumulation generation processing, order x ( t ) x ( ti) (2) k (1) (0) k i1 DOI /IJSSST.a.16.2A ISSN: x online, print

3 Get new data columns (0) (1) (1) (1) x ( t ) i x ( t1), x ( t2), x ( tn) (3) Here, only discuss contain a variable grey dynamic model of a first order form, the GM (1, 1), its form is: (1) dx (1) ax (4) dt Type a, µ for the modeling processes of unknown parameters and internal variables, x (1) as the original data through accumulation generation processing to get new data columns. B. ARIMA Time Series Model Among them, for yt sample value; Φ0,Φ1, Φp as the regression coefficient; εt interference for the system, is to obey the normal distribution N (0, δ2) white noise sequence. For non-stationary time series, the first to be-d order difference make it smooth, again through the autocorrelation function and partial autocorrelation function of the nature of the judgment, determine the value of p, q, and then can use SPSS software for the calculation of model parameters, related to the related forecast results. ARIMA (p, d, q) Model called the difference Autoregressive Moving Average Model (Autoregressive Integrated Moving Average Model, shorthand ARIMA).Among them, p, q are respectively autoregressive process AR and MA order of moving average process, the model is suitable for non-stationary time series prediction. The general expression for: y y y (5) t 0 1 t1 p tp t 1 t1 q tp C. The Establishment of The Combination Forecast Model Based on the idea of information fusion, respectively using grey prediction model and ARIMA predict two methods to get their forecast results, then its mean, as a grey prediction system input for the next phase of the forecast, and its actual value as the desired output were analyzed (Geng Liyan, Zhang Tianwei, Zhao Peng, 2012) [8]. Finally, the three kinds of traffic forecast method of prediction effect were analyzed. Evaluation using absolute error MAPE values to evaluate the average percentage of indicators, the calculation formula is:. n yi yi 1 EMAP 100% (6) n y i1 Among them, yi is the actual measured value of flow; was traffic forecast; n for the sample size. IV. THE METHODOLOGY A. The Combination of Fuzzy Logic and Neural Network Fuzzy logic and neural network are two very different field of study, but their purpose is, because they are used in the research of artificial intelligence. Fuzzy system and neural network are equivalent in theory, but they all have their own advantages and disadvantages in practice (Wang Zucheng, Liu ling, 2010) [9]. Table 1 shows the comparison i. y i of fuzzy logic and neural network. Thus, fuzzy logic and neural network is relevant and complementary to each other in many ways. Fuzzy neural network to learn the essence of living cybernetics, for information storage and processing is distributed and parallel type. In this way, all sorts of neurons in each part on the basis of information sharing and support each other, complement each other, independently from its input at the connection of other neurons collecting input, and calculate the output, and then pass it to a layer (or other) neurons, as an input of them, or as the output of the entire model, so as to give strong fault-tolerant model fault resistance and association ability, make it won't because some damage to neurons and seriously affect its overall performance, also won't because the input signal by a certain degree of noise pollution and serious distortion of the output. As a result, the fuzzy neural network has robustness (Fu Hui, Xu Lunhui, Hu Gang, 2010) [10]. TABLE I COMPARISON OF FUZZY LOGIC AND NEURAL NETWORK The fuzzy logic The neural network Knowledge Membership function representation according to Distributed said Fault tolerance Fault tolerance Fault tolerance Incomplete data processing can can Operation The calculation of subordinate function The interaction of neurons Reasoning The combination of Since the control of learning fuzzy rules function Function Simple calculation processing, language values and numerical processing, fuzzy decision and fuzzy control, identification, etc. Feature extraction and optimization, associative memory, cluster analysis, classification, scheduling, prediction, evaluation, function approximation calculation, etc. For the combination of fuzzy logic system and neural network, we will take the following several ways: will the weights of neural network fuzzy; the neural network input blurred; before the combination of the two ways. The combination of artificial neural network and fuzzy logic system in general is divided into two ways: first, the master-slave type combination: in this kind of structure, the fuzzy logic system with artificial neural network once a master (Yuan Yuhao, Zhang Guangming, 2010) [11]. Usually by the fuzzy logic system first to deal with the data sample, again by the artificial neural network to further improve, or vice versa. Master-slave structure of fuzzy neural network is the biggest characteristic of the fuzzy logic system and the other is the independency of artificial neural network., fusion model combined with 2: in this kind of structure, the fuzzy logic system with artificial neural network of the primary and secondary points, but the sometimes-complex mix-and-match I have you, to achieve with one another can t realize the function or difficult to achieve. On the study of fuzzy neural network, we will focus mainly concentrated in two aspects: using fuzzy reasoning technology speeds up the neural network learning speed, and then use the neural network to construct the high performance of fuzzy logic system and neural network was DOI /IJSSST.a.16.2A ISSN: x online, print

4 used to extract fuzzy rules and fuzzy logic system also has a certain degree of self-learning ability. Fuzzy logic and neural network combining fuzzy neural network system has the following two kinds of structure model: the first kind of structural model as shown in figure 4 (a), by fuzzy interface language variables as input into the neural network of networks, the final results produced by the neural network output or decisions. The second structure model as shown in figure 4 (b), neural network is used to optimize and adjust the member functions of fuzzy system (Zhang Shuqing, Jia Jian, Gao min, 2010) [12]. conventional Fuzzy Neural Network is generally referred to as RFNN (Regular Fuzzy Neural Network) or is called a FNN (Fuzzy Neural Network) : in general, all the conventional Fuzzy Neural Network (FNN for short). Hybrid fuzzy neural network Hybrid fuzzy neural network (hereinafter referred to as HFNN (Hybrid Fuzzy Neural Network).Hybrid fuzzy neural network, any operation can be used to aggregate data, any function can be used as a transfer function to generate the output of the network. For special applications use can choose the related and effective aggregation operation and the transfer function. In the conventional fuzzy neural network, namely standard fuzzy neural networks, data aggregation method using fuzzy addition or multiplication operation the transfer functions using S function (ZuoDan, 2010) [14]. V. RESULTS ANALYSIS Figure 4. Structure model (a) Figure 4. Structure model (b) B. A Fuzzy Neural Network Classification Fuzzy neural network has a very wide range of scope, it includes both with fuzzy neural network of neurons, including only to realize fuzzy logic reasoning and input as well as the weights for the accurate values of neural network, also including the fuzzy input and fuzzy output or fuzzy weights of neural network. In a nutshell, a fuzzy neural network including, the neural network and fuzzy information, processing to neural network as a tool to implement the fuzzy system two kinds big (Yu Jianglong, Peng Yuehua, 2011) [13]. In the case of worshiping values are fuzzy sets, we can convert it into FNNi i=1, 2, 3. C. The Main Form of Fuzzy Neural Network Logic and fuzzy neural network Logic fuzzy neural network is composed of fuzzy logic neurons. Fuzzy logic neurons are a fuzzy weight, and can be the input of fuzzy neurons signal execution logic operation, performed by the fuzzy neuron fuzzy arithmetic logic operation, arithmetic operations and other operations. In any case, is the basis of the traditional fuzzy neuron, they can be derived from the traditional neuron. The arithmetic of fuzzy neural network Arithmetic of fuzzy neural network can be the input fuzzy signal to perform arithmetic operations, and with fuzzy neural network weights. Usually, the arithmetic of fuzzy neural network is also known as the conventional fuzzy neural networks, or the standard fuzzy neural network. The A. Data Preprocessing In the use of neural network in prediction experiments, if not transformation processing, will be the absolute error in the output value increase. In addition, we assume that the forecast experiments the original data of the curve is a smooth, even if they don't do any processing, the prediction results will be ideal. But in the actual cases, the data are often incomplete or inconsistent, show the complex nonlinear system, which makes the experiment results are always poor (Gao Yong, Chen Feng, 2008) [15]. Secondly, a neural network input or output often have different dimension, if not properly transform processing will bring great influence network training. Based on the above reasons, in order to improve the effect of the experiment, we need the data pretreatment technology. In this paper, the data pretreatment method is as follows: ' ' xl ( xl min( X))/(max( X) min( X)), xl [0,1] (7) Assume that the time series for X=(x1, x2,, xn), X RN, (7) can use of the normalized processing time sequence. xl is the normalized data, xl for the raw data. B. Predict Evaluation Standard In order to reflect the prediction effect from different sides, put forward some different error evaluation criteria, these principles in all areas of prediction has a broad applicability. Main indicators: root mean square error and relative mean square error, the normalized absolute error, accuracy, equal coefficient, deviation, etc., this paper adopts the prediction performance evaluation index for the regularization coefficient of root mean square error and equal. For the desired output signal assume In order to reflect the prediction effect from different sides, put forward some different error evaluation criteria, these principles in all areas of prediction has a broad applicability. Main indicators: root mean square error and relative mean square error, the normalized absolute error, accuracy, equal coefficient, deviation, etc., this paper adopts the prediction performance DOI /IJSSST.a.16.2A ISSN: x online, print

5 evaluation index for the regularization coefficient of root mean square error and equal. yt (),( t1,2,, S) For the desired output signal assume, yt (),( t1,2,, S) for the model prediction output signal, S as sample number. Regularization root mean square error ENRMSE: S 2 1 ENRMSE y() t y() t (8) 2 ( S 1) t1 Said σ target time series of standard variance, ENRMSE reflects the average deviation of the predicted values and the observed value, the correlation between the degree and ENRMSE 0. Equal coefficient Eequ: E equ 1 S ( yt ( ) yt ( )) t1 S S 2 2 t1 t1 y () t y () t Equal coefficient reflects the degree of corresponding points close to the prediction curve and measured curve, 0 < Eequ 1, its value is close to 1 said predicted more close to the observed value, no error is equal to 1. C. A Fuzzy Neural Network Method To Predict The BP neural network is a kind of fuzzy neural network this article using the BP neural network to traffic forecast model is set up. BP network is a one-way transmission of multi-layer forward network, according to the functions and features of the BP neural network, the structure prediction model. In this section, the network layer selected three layers, due to the input vector with four elements, namely there are four network input layer neurons. Number of nodes in the hidden layer network selection is based on Kolmogorov's theorem. Assuming three layers BP network, and its input layer has n units, the number of nodes in the hidden layer of 2 n + 1. In this case because of the four unit of input layer, therefore take nine implicit stratifications. Because the output of one dimensional projections of the columns, so the output neurons are one. The transfer function of input layer to hidden layer uses logarithmic function transfer function of hidden layer to output layer neurons using linear transfer function. Number of network training set to 5000 times, training target error vector is set to η is set to 0.1, the initial weights of random Numbers (1, 1).According to the above settings so as to construct the type neural network model. BP network has a variety of improved algorithm, the improved algorithm of BP network to some extent, overcome the drawbacks of the standard BP algorithm, such as long training time, easily trapped in local minimum value, the high to the requirement of the training sample. Which effect best is LM (Levenberg Marquardt) algorithm. LM algorithm is the combination of Newton algorithm and gradient descent algorithm, under the condition of guarantee the performance of network, can accelerate the convergence speed of the network. 2 (9) TABLE2 ALGORITHM BASED ON BP NETWORK TRAFFIC PREDICTION Prediction method The training steps Regularization root mean square error Equal coefficient The standard BP network algorithm The additional momentum improved algorithm The improved algorithm of adaptive adjustment of parameters Resilient BP algorithm Conjugate gradient algorithm LM algorithm Figure 4. Algorithm based on BP Network Traffic Prediction Reflects the table 4 under the two kinds of error evaluation criterion used in this article, type feed forward neural network and the predictive results of the improved algorithm. By the data in the table it can be seen that using the standard BP network algorithm steps training most, namely the minimum target error when the speed of the slowest, the forecasting result of regularization root mean square error and relative error less than other algorithms, the equal coefficient is smaller, and the real value of deviation degree is bigger. LM algorithm not only convergence rate is fast, and the prediction error is small, and the real value of fitting degree is more outstanding, overall using LM algorithm achieved good prediction effect. VI. CONCLUSION In this paper based on the study of the basic features of Inner Mongolia grassland tourism highway, in Inner Mongolia grassland tourism traffic for the analysis of the system, and in theory research of grassland tourism highway traffic prediction modeling method and its application. Through before the existing prediction methods and the analysis of the characteristics of the model, combined with the characteristics of Inner Mongolia grassland tourism highway, we chose by fuzzy network control algorithm combined with BP neural network in forecasting methods and models, and use the LM algorithm is optimized. DOI /IJSSST.a.16.2A ISSN: x online, print

6 This paper first discussed the predictability of traffic flow, summarizes the commonly used methods for traffic flow prediction, and then studied the way of the combination of fuzzy logic and neural network. In the traffic flow prediction model is set up, you must determine the input and output of the network. The usual way is to rely on the setting of the experience and intuition of the experimenter and the result is not a definite standard, thus easy to cause the result is not accurate or dimension disaster. This article will mix of theory is introduced into traffic flow forecast, first of all, by calculating index determine its chaotic sex, and calculate the optimal embedding dimension and time delay, thus setting network input and output, and through the recursive figure of data used in this article has carried on the analysis of predictability. According to the theory of phase space reconstruction analysis, forecast the effect not only with the select method and with the network structure of the set have a close relationship. Taken together, the chaos theory and fuzzy neural networks combine both dealing with traffic flow forecasting is a kind of good method. ACKNOWLEDGMENT This work was supported by a grant from the National Natural Science Foundation of China (Grant Nos , , ),and Humanities and Social Science Research Project of Department of Education of Anhui Province(Grant No.SK2015A487,SK2016SD51), Anhui provincial high school teaching research project (No.2015jyxm320) The backbone of excellent young talents at home and abroad visit the school training project of Anhui provincial high school (No.gxfxZD ) Talent Research Fundation of Hefei University(NO.2015RC12). REFERENCES [1] Chen Zhe, Feng Tianjin, Zhang Haiyan. Based on the wavelet neural network analysis and phase space reconstruction of chaotic time series, Journal of computer research and development, vol. 38, No. 05, pp , [2] Jiang Yaping, Guo Junliang. The intersection traffic forecasting model based on markov process study,journal of zhengzhou institute of light industry (natural science edition), vol. 16, No. 05, pp , [3] Dong Chaojun, Liu Zhiyong, Qui Zulian. Prediction of Traffic Flow in Real-time Based on Chaos Theory,Information and Control, vol. 10, No. 04, pp , [4] Shen Guojiang, Wang Xiaohu, Kong Xiangjie. The intelligent combination of short-term traffic flow prediction model and application, System Engineering Theory and Practice, vol. 31, No. 03, pp , [5] Anuja Nagare, Shalini Bhatia. Traffic flow control using neural network, International Journal of Applied Information Systems, vol. 16, No. 05, pp , [6] Ma Yunyun. Inner Mongolia grassland tourism highway network planning study,huhhot: Inner Mongolia agricultural university, vol. 14, No. 05, pp ,2007 [7] Che Chiang Hsu, Chia Yon Chen. Applications of Improved Grey Prediction Model for Power Demand Forecasting, Energy Conversion and Management, vol. 16, No. 05, pp , [8] Che Chiang Hsu,Chia Yon Chen. Applications of Improved Grey Prediction Model for Power Demand Forecasting, Energy Conversion and Management, vol. 44, No. 14, pp , [9] Geng Liyan, Zhang Tianwei, Zhao Wei. LS-SVM based on grey correlation analysis of railway freight volume forecasting, Journal of railway, vol. 34, No. 04, pp. 1-6, [10] Wang Zucheng, Liu ling. Comparison and research of tourist flow prediction models in Huangshan scenic area, for example, Journal of anhui normal university, natural sciences, vol. 10, No. 03, pp , [11] Fu Hui, Xu Lunhui, Hu Gang etc. Based on the Sugeno type neural fuzzy system state of traffic flow prediction algorithm, Control theory and applications, vol. 27, No. 12, pp , [12] Yuan Yuhao, Zhang Guangming. T-S fuzzy generalized system research review, Journal of automation, vol. 4, No. 07, pp , [13] Zhang Shuqing, Jia Jian, Gao min, etc. The chaos phase space reconstructed by time series parameters selection research, Journal of physics, vol. 59, No. 03, pp , [14] Yu Jianglong, Peng Yuehua. By the method of mutual information and Cao method can forecast of ENSO, Journal of meteorological science and technology, vol. 33, No. 01, pp. 9-12, [15] Zuo Dan. Based on GM (1, 1) model of Chongqing domestic passenger volume forecast and influence factors analysis, Journal of regional economy, vol. 16, No. 04, pp , [16] Gao Yong, Chen Feng. No detector based on fuzzy reasoning intersection short-time traffic flow prediction, Computer simulation. vol. 26, No. 05, pp , DOI /IJSSST.a.16.2A ISSN: x online, print