A similarity-based learning algorithm for fuzzy system identification with a two-layer optimization scheme

This paper presents a similarity-based fuzzy learning approach with a two-layer optimization scheme to make fuzzy systems more compact and accuracy. Two ways to improve fuzzy learning algorithms are considered in this paper, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned, the purpose aims at refining the fuzzy rule base by the similarity analysis of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy rules. Through the similarity analysis, the complete rules can be probably kept by decreasing the redundant rules in the rule base of fuzzy systems. Moreover, the optimization scheme can be regarded as a two-layer parameters optimization in the entire work, because the parameters of the initial fuzzy model have been fine tuning by two phases gradation on layer for discovering a better local minimum.

[1]  Meng Joo Er,et al.  Dynamic fuzzy neural networks-a novel approach to function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[3]  Y Lu,et al.  A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks , 1997, Neural Computation.

[4]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

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

[6]  John Yen,et al.  Improving the interpretability of TSK fuzzy models by combining global learning and local learning , 1998, IEEE Trans. Fuzzy Syst..

[7]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Rui Pedro Paiva,et al.  Interpretability and learning in neuro-fuzzy systems , 2004, Fuzzy Sets Syst..

[9]  T. Martin McGinnity,et al.  An on-line algorithm for creating self-organizing fuzzy neural networks , 2004, Neural Networks.

[10]  Xiao-Jun Zeng,et al.  A hybrid learning algorithm with a similarity-based pruning strategy for self-adaptive neuro-fuzzy systems , 2009, Appl. Soft Comput..

[11]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[12]  Xiao-Jun Zeng,et al.  An incremental construction learning algorithm for identification of T-S Fuzzy Systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[13]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[14]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[15]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Augustin-Louis Cauchy,et al.  ANALYSE MATHÉMATIQUE. – Méthodc générale pour la résolution des systèmes d'équations simultanées , 2009 .

[17]  Kwang Bo Cho,et al.  Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction , 1996, Fuzzy Sets Syst..

[18]  W. Peizhuang.  Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

[19]  Shie-Jue Lee,et al.  A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning , 2003, IEEE Trans. Fuzzy Syst..

[20]  Uzay Kaymak,et al.  Similarity measures in fuzzy rule base simplification , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[21]  Rudolf Kruse,et al.  Neuro-fuzzy systems for function approximation , 1999, Fuzzy Sets Syst..

[22]  Wan-Jui Lee,et al.  A TSK-type neurofuzzy network approach to system modeling problems , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[23]  Giovanna Castellano,et al.  A self-organizing neural fuzzy inference network , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[24]  Isao Hayashi,et al.  A learning method of fuzzy inference rules by descent method , 1992 .

[25]  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..

[26]  Tzung-Pei Hong,et al.  Induction of fuzzy rules and membership functions from training examples , 1996, Fuzzy Sets Syst..

[27]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[28]  Plamen P. Angelov,et al.  Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[29]  M. Delgado,et al.  An inductive learning procedure to identify fuzzy systems , 1993 .

[30]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[31]  Lawrence O. Hall,et al.  Generating fuzzy rules from data , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[32]  Paramasivan Saratchandran,et al.  Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction , 2006, Fuzzy Sets Syst..

[33]  T. Martin McGinnity,et al.  An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network , 2005, Fuzzy Sets Syst..

[34]  John Yen,et al.  Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter , 1999, Fuzzy Sets Syst..

[35]  Visakan Kadirkamanathan,et al.  A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.