KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning

Authors:
K. Tian;B. Guo;G. Liu;I. Mitchell;D. Cheng;W. Zhao
Affiliations:
School of Automation, Harbin Engineering University, Harbin, China;School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin China;School of Computer Engineering, Nanyang Technological University, Singapore;School of Engineering and Information Sciences, Middlesex University, London NW4 4BT, UK;School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin China;School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin China
Venue:
Neurocomputing
Year:
2013

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Abstract

The Cerebellar Model Articulation Controller (CMAC) possesses attractive properties of fast learning and simple computation. In application, the size of its association vector is always reduced to economize the memory requirement, greatly constraining its modeling capability. The kernel CMAC (KMAC), which provides an interpretation for the traditional CMAC from the kernel viewpoint, not only strengthens the modeling capability without increasing its complexity, but reinforces its generalization with the help of a regularization term. However, the KCMAC suffers from the problem of selecting its hyperparameter. In this paper, the Bayesian Ying-Yang (BYY) learning theory is incorporated into KCMAC, referred to as KCMAC-BYY, to optimize the hyperparameter. The proposed KCMAC-BYY achieves the systematic tuning of the hyperparameter, further improving the performance in modeling and generalization. The experimental results on some benchmark datasets show the prior performance of the proposed KCMAC-BYY to the existing representative techniques.