A self-adaptive neural fuzzy network with group-based symbiotic evolution and its prediction applications

In this paper, we propose a self-adaptive neural fuzzy network with group-based symbiotic evolution (SANFN-GSE) method. A self-adaptive learning algorithm consists of two major components. First, a self-clustering algorithm (SCA) identifies a parsimonious internal structure. An internal structure is said to be parsimonious in the sense that the number of clusters (fuzzy rules) is equal to the true number of clusters in a given training data set. The proposed SCA is an online method and is a distance-based connectionist clustering method. Unlike the traditional cluster techniques that only consider the total variation to updates the only one mean and deviation. The proposed SCA method considers the variation of each dimension for the input data. Second, a group-based symbiotic evolution learning (GSE) method is used to adjust the parameters for the desired outputs. The GSE method is different from traditional GAs (genetic algorithms), with each chromosome in the GSE method representing a fuzzy system. Moreover, in the GSE method, there are several groups in the population. Each group represents a set of the chromosomes that belong to a cluster computing by the SCA. In this paper we used numerical time series examples (one-step-ahead prediction, Mackey-Glass chaotic time series, and sunspot number forecasting) to evaluate the proposed SANFN-GSE model. The performance of the SANFN-GSE model compares excellently with other existing models in our time series simulations.

[1]  Hiok Chai Quek,et al.  GenSoFNN: a generic self-organizing fuzzy neural network , 2002, IEEE Trans. Neural Networks.

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[3]  Alan S. Perelson,et al.  Searching for Diverse, Cooperative Populations with Genetic Algorithms , 1993, Evolutionary Computation.

[4]  Risto Miikkulainen,et al.  Efficient Reinforcement Learning through Symbiotic Evolution , 1996, Machine Learning.

[5]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986, Encyclopedia of Big Data.

[6]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[7]  Hak-Keung Lam,et al.  A novel genetic-algorithm-based neural network for short-term load forecasting , 2003, IEEE Trans. Ind. Electron..

[8]  Chia-Feng Juang,et al.  A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms , 2002, IEEE Trans. Fuzzy Syst..

[9]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[10]  Chin-Teng Lin,et al.  Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design , 2022 .

[11]  Michio Sugeno,et al.  On stability of fuzzy systems expressed by fuzzy rules with singleton consequents , 1999, IEEE Trans. Fuzzy Syst..

[12]  A. Lapedes,et al.  Nonlinear Signal Processing Using Neural Networks , 1987 .

[13]  H. Tong.  Non-linear time series. A dynamical system approach , 1990 .

[14]  S Hendry,et al.  Searching for diversity. , 1997, Australian nursing journal (July 1993).

[15]  Kishan G. Mehrotra,et al.  Sunspot numbers forecasting using neural networks , 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.

[16]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[17]  R. Scott Crowder,et al.  Predicting the Mackey-Glass Timeseries With Cascade-Correlation Learning , 1990 .

[18]  H. Takagi,et al.  Integrating Design Stages of Fuzzy Systems using Genetic Algorithms 1 , 1993 .

[19]  H. Ishibuchi.  Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases , 2004 .

[20]  Cheng Jian Lin,et al.  Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks(Optimization and Control)( Nonlinear Theory and its Applications) , 2003 .

[21]  Giovanna Castellano,et al.  A neuro-fuzzy network to generate human-understandable knowledge from data , 2002, Cognitive Systems Research.

[22]  Junhong Nie,et al.  Rule-based modeling: fast construction and optimal manipulation , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[23]  Don-Lin Yang,et al.  An efficient Fuzzy C-Means clustering algorithm , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[24]  Michel Pasquier,et al.  POPFNN-CRI(S): pseudo outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[25]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[26]  Chih-Hong Lin,et al.  Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive , 2001, IEEE Trans. Fuzzy Syst..

[27]  J. Chiang,et al.  A new kernel-based fuzzy clustering approach: support vector clustering with cell growing , 2003, IEEE Trans. Fuzzy Syst..

[28]  Li Liu,et al.  Speaker identification using hybrid LVQ-SLP networks , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[29]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.