A self-organizing recurrent fuzzy CMAC model for dynamic system identification

This paper presents a self-organizing recurrent fuzzy cerebellar model articulation controller (RFCMAC) model for identifying a dynamic system. The recurrent network is embedded in the self-organizing RFCMAC by adding feedback connections with a receptive field cell to the RFCMAC, where the feedback units act as memory elements. A nonconstant differentiable Gaussian basis function is used to model the hypercube structure and the fuzzy weight. An online learning algorithm is proposed for the automatic construction of the proposed model during the learning procedure. The self-constructing input space partition is based on the degree measure to appropriately determine various distributions of the input training data. A gradient descent learning algorithm is used to adjust the free parameters. The advantages of the proposed RFCMAC model are summarized as (1) it requires much lower memory requirement than other models; (2) it selects the memory structure parameters automatically; and (3) it has better identification performance than other recurrent networks. © 2008 Wiley Periodicals, Inc.

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

[2]  W. T. Miller,et al.  An overview of the CMAC neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[3]  Chih-Ming Chen,et al.  A self-organizing HCMAC neural-network classifier , 2003, IEEE Trans. Neural Networks.

[4]  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).

[5]  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..

[6]  Mo-Yuen Chow,et al.  A self-organized CMAC controller , 1994, Proceedings of 1994 IEEE International Conference on Industrial Technology - ICIT '94.

[7]  Chin-Teng Lin,et al.  A recurrent self-organizing neural fuzzy inference network , 1999, IEEE Trans. Neural Networks.

[8]  Kao-Shing Hwang,et al.  Smooth trajectory tracking of three-link robot: a self-organizing CMAC approach , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Zhen Ye,et al.  A hybrid fuzzy cerebellar model articulation controller based autonomous controller , 2002, Comput. Electr. Eng..

[10]  A. Kolcz.  Application of the CMAC input encoding scheme in the N-tuple approximation net , 1994 .

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

[12]  Jen-Yang Chen.  A VSS-type FCMAC controller , 2001, 10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297).

[13]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[14]  C.-S. Lin,et al.  Use of adaptive resolution for better CMAC learning , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

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

[16]  W. Thomas Miller,et al.  Real-time dynamic control of an industrial manipulator using a neural network-based learning controller , 1990, IEEE Trans. Robotics Autom..

[17]  Jianjuen Hu,et al.  Self-organizing CMAC neural networks and adaptive dynamic control , 1999, Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014).

[18]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[19]  Frank L. Lewis,et al.  CMAC neural networks for control of nonlinear dynamical systems: Structure, stability and passivity , 1997, Autom..

[20]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[21]  C. S. Berger.  Linear splines with adaptive mesh sizes for modelling nonlinear dynamic systems , 1994 .