Adaptive filter design using recurrent cerebellar model articulation controller

Authors:
Chih-Min Lin;Li-Yang Chen;Daniel S. Yeung
Affiliations:
Department of Electrical Engineering, Yuan Ze University, Chung-Li, Taiwan;Test Assistance Group, Teradyne, Inc., Hsinchu, Taiwan;School of Computer Science and Engineering, South. China University of Technology, Guangzhou, China
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RC-MAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are online adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.