Cmac: Reconsidering an Old Neural Network
نویسنده
چکیده
Cerebellar Model Articulation Controller (CMAC) has some attractive features: 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. Among them the most important ones are about its modelling and generalization capabilities. The limits of its modelling capability were addressed in the literature and recently a detailed analysis of its generalization property was given. The results show that there are differences between the one-dimensional and the multidimensional versions of CMAC. The modelling capability of a multidimensional network is inferior to that of the one-dimensional one. This paper discusses the reasons of this difference and suggests a new kernel-based interpretation of CMAC. It shows that a one-dimensional binary CMAC can be considered as an SVM with second order B-spline kernel function. Applying this approach the paper shows that one-dimensional and multidimensional CMACs can be constructed with similar modelling capability. Copyright © 2003 IFAC
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تاریخ انتشار 2003