A simple and converged structure of addressing technique for CMAC/spl I.bar/GBF

This paper proposed a simple and converged structure of addressing technique for CMAC/spl I.bar/GBF (cerebellar model articulation controller with general basis function). The structure of addressing technique for original CMAC/spl I.bar/GBF has two major problems: one is the structure of addressing technique does not have a fixed procedure to find it, the other is the memory size growing too large in the high dimension input space systems. In this paper, the procedure to find the simple structure is developed. The mathematical basis for this simple structure is derived and the learning convergence is guaranteed. Simulations for the simple structure of CMAC/spl I.bar/GBF are performed to demonstrate the learning performance and the capability in reducing the memory size of system.

[1]  Hsin-Chia Fu,et al.  Cascade-CMAC neural network applications on the color scanner to printer calibration , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[2]  Ying Chen,et al.  Identifying chaotic systems via a Wiener-type cascade model , 1997 .

[3]  Chun-Shin Lin,et al.  Learning convergence of CMAC technique , 1997, IEEE Trans. Neural Networks.

[4]  Chun-Shin Lin,et al.  A new neural network structure composed of small CMACs , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

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

[6]  Mo-Yuen Chow,et al.  On the training of a multi-resolution CMAC neural network , 1997, ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics.

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

[8]  Chun-Shin Lin,et al.  CMAC with General Basis Functions , 1996, Neural Networks.

[9]  Wei-Song Lin,et al.  A method of clustering quantization for better training of CMAC , 1996 .

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

[11]  H. Sugimoto,et al.  A new scheme for maximum photovoltaic power tracking control , 1997, Proceedings of Power Conversion Conference - PCC '97.

[12]  Christopher J. Harris,et al.  An intelligent driver warning system for vehicle collision avoidance , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[13]  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).

[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]  Kostas Kalaitzakis,et al.  Development of a microcontroller-based, photovoltaic maximum power point tracking control system , 2001 .

[16]  T. Yamamoto,et al.  Intelligent controller using CMACs with self-organized structure and its application for a process system , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.