NDLI: Kernel CMAC With Improved Capability

Content Provider	IEEE Xplore Digital Library
Author	Horvath, G. Szabo, T.
Copyright Year	1996
Abstract	The cerebellar model articulation controller (CMAC) has some attractive features, namely fast learning capability and the possibility of efficient digital hardware implementation. Although CMAC was proposed many years ago, several open questions have been left even for today. The most important ones are about its modeling and generalization capabilities. The limits of its modeling capability were addressed in the literature, and recently, certain questions of its generalization property were also investigated. This paper deals with both the modeling and the generalization properties of CMAC. First, a new interpolation model is introduced. Then, a detailed analysis of the generalization error is given, and an analytical expression of this error for some special cases is presented. It is shown that this generalization error can be rather significant, and a simple regularized training algorithm to reduce this error is proposed. The results related to the modeling capability show that there are differences between the one-dimensional (1-D) and the multidimensional versions of CMAC. This paper discusses the reasons of this difference and suggests a new kernel-based interpretation of CMAC. The kernel interpretation gives a unified framework. Applying this approach, both the 1-D and the multidimensional CMACs can be constructed with similar modeling capability. Finally, this paper shows that the regularized training algorithm can be applied for the kernel interpretations too, which results in a network with significantly improved approximation capabilities
Page Count	15
File Size	938813
Starting Page	124
Ending Page	138
File Format	PDF
ISSN	10834419
Volume Number	37
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Hardware Multidimensional systems Neural networks Multi-layer neural network Seismic measurements Information systems Nonlinear filters Interpolation Approximation algorithms regularization Cerebellar model articulation controller (CMAC) generalization error kernel machines modeling neural networks
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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