Fuzzy CMAC structures

Cerebellum Model Articulation Controller (CMAC) is known as a feedforward Neural Network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that its operation is interpretable using fuzzy rules. Fuzzy CMAC has not a unique structure in literature and there are differences in many aspects as membership function, memory layered structure, deffuzification and the fuzzy system applied. Discussing these, this paper reviews fuzzy CMAC different structures in literature.

[1]  Hongjun Lu,et al.  Fuzzy system and CMAC network with B-spline membership/basis functions are smooth approximators , 2003, Soft Comput..

[2]  A. Barto,et al.  Models of the cerebellum and motor learning , 1996 .

[3]  Isao Hayashi,et al.  Formulation of CMAC-fuzzy system , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[4]  Derek A. Linkens,et al.  A fuzzified CMAC self-learning controller , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[5]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[6]  L. G. Kraft,et al.  Comparison of CMAC architectures for neural network based control , 1990, 29th IEEE Conference on Decision and Control.

[7]  Takeshi Furuhashi,et al.  Cerebellar model arithmetic computer with pseudo-bacterial genetic algorithm and its hardware acceleration , 2004, Systems and Computers in Japan.

[8]  Zhu,et al.  Fault-tolerant Control of Nonlinear System Using Credit Assign Fuzzy CMAC , 2006 .

[9]  Daming Shi,et al.  FCMAC-BYY: Fuzzy CMAC Using Bayesian Ying–Yang Learning , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Zhiyong Tang,et al.  Fuzzy CMAC Model Predictive Control , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[11]  Wen Yu,et al.  Hierarchical Fuzzy CMAC for Nonlinear Systems Modeling , 2008, IEEE Transactions on Fuzzy Systems.

[12]  Lin Chun-Shin,et al.  CMAC with General Basis Functions. , 1996, Neural networks : the official journal of the International Neural Network Society.

[13]  Lotfi A. Zadeh,et al.  Fuzzy logic and the calculus of fuzzy if-then rules , 1992, [1992] Proceedings The Twenty-Second International Symposium on Multiple-Valued Logic.

[14]  Chun-Shin Lin,et al.  Integration of CMAC technique and weighted regression for efficient learning and output differentiability , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[15]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[16]  Daijin Kim,et al.  A design of CMAC-based fuzzy logic controller with fast learning and accurate approximation , 2002, Fuzzy Sets Syst..

[17]  Nigel M. Allinson,et al.  Basis function models of the CMAC network , 1999, Neural Networks.

[18]  James S. Albus,et al.  I A New Approach to Manipulator Control: The I Cerebellar Model Articulation Controller , 1975 .

[19]  Cheng-Jian Lin,et al.  A novel hybrid learning algorithm for parametric fuzzy CMAC networks and its classification applications , 2008, Expert Syst. Appl..

[20]  Hiok Chai Quek,et al.  FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC , 2006, IEEE Transactions on Neural Networks.

[21]  Wang Shengwei,et al.  Variable structure control of electrohydraulic servo systems using fuzzy CMAC neural network , 2003 .

[22]  Mohammad Teshnehlab,et al.  Predictive Adaptive Control of Nonlinear Multivariable Systems Using Fuzzy CMAC , 2008, 2008 International Conference on Computational Intelligence for Modelling Control & Automation.

[23]  Feng Qian,et al.  Fuzzy CMAC and its application , 2000, Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No.00EX393).

[24]  W. S. Mischo A CMAC-type neural memory for control applications , 1996, Proceedings of Fifth International Conference on Microelectronics for Neural Networks.

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

[26]  Paulo E. M. de Almeida,et al.  Modified Fuzzy-CMAC Networks with Clustering-based Structure , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[27]  Yau-Hwang Kuo,et al.  A fuzzy CMAC model for color reproduction , 1997, Fuzzy Sets Syst..

[28]  Lotfi A. Zadeh,et al.  The Calculus of Fuzzy If/Then Rules , 1992, Fuzzy Days.

[29]  Zne-Jung Lee,et al.  Robust and fast learning for fuzzy cerebellar model articulation controllers , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Lei Xu,et al.  BYY harmony learning, structural RPCL, and topological self-organizing on mixture models , 2002, Neural Networks.

[31]  Aníbal R. Figueiras-Vidal,et al.  Generalizing CMAC architecture and training , 1998, IEEE Trans. Neural Networks.

[32]  Chiman Kwan,et al.  Machine performance degradation monitoring using fuzzy CMAC , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[33]  R. Tilani,et al.  Univariate time series forecasting with fuzzy CMAC , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[34]  M.G. Simoes,et al.  Neural optimal control of PEM fuel cells with parametric CMAC networks , 2003, IEEE Transactions on Industry Applications.

[35]  James M. Keller,et al.  Neural network implementation of fuzzy logic , 1992 .

[36]  Man Ieee Systems,et al.  IEEE transactions on systems, man and cybernetics. Part B, Cybernetics , 1996 .

[37]  Chi-Cheng Jou,et al.  A fuzzy cerebellar model articulation controller , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[38]  Z. Jason Geng,et al.  Missile Control Using Fuzzy Cerebellar Model Arithmetic Computer Neural Networks , 1997 .

[39]  Guo Chen,et al.  Multimode fuzzy-CMAC intelligent controller , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[40]  Hiok Chai Quek,et al.  FCMAC-EWS: A bank failure early warning system based on a novel localized pattern learning and semantically associative fuzzy neural network , 2008, Expert Syst. Appl..

[41]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[42]  Ching-Chang Wong,et al.  A fuzzy CMAC structure and learning method for function approximation , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[43]  Hui Li,et al.  General fuzzified CMAC-based model reference adaptive control for ship steering using float-encoding genetic algorithm , 2006 .

[44]  In-Won Lee,et al.  CEREBELLAR MODEL ARTICULATION CONTROLLER (CMAC) FOR SUPPRESSION OF STRUCTURAL VIBRATION , 2002 .

[45]  Paulo Eduardo Maciel de Almeida,et al.  Neural optimal control of PEM-fuel cells with parametric CMAC networks , 2003 .

[46]  Xiaocheng Shi,et al.  A new model of fuzzy CMAC network with application to the motion control of AUV , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[47]  Ching-Hung Lee,et al.  A Novel Wavelet-based-CMAC Neural Network Controller for Nonlinear Systems , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[48]  M.G. Simoes,et al.  Parametric CMAC networks: fundamentals and applications of a fast convergence neural structure , 2002, Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344).

[49]  D.A. Handelman,et al.  Theory and development of higher-order CMAC neural networks , 1992, IEEE Control Systems.

[50]  Daming Shi,et al.  Product Demand Forecasting with a Novel Fuzzy CMAC , 2007, Neural Processing Letters.

[51]  Ching-Tsan Chiang,et al.  Neural networks composed of single-variable CMACs , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).