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Fourier analysis of the generalized CMAC neural network

✍ Scribed by Francisco J González-Serrano; Anı́bal R Figueiras-Vidal; Antonio Artés-Rodriguez

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
90 KB
Volume
11
Category
Article
ISSN
0893-6080

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✦ Synopsis

The Cerebellar Model Articulation Controller (CMAC) is a simple and fast neural network: these characteristics have extended its successful applications, while the analysis of its representation capabilities, as for many other neural networks, did not follow a similar development.

In this article we discover the close parallelism between the representation of a function by a Generalized CMAC (GCMAC) and Nyquist sampling theory: discussing the role of different parameters and components of the network according to this similarity. The consideration of a representative example shows how the parallelism can be used to design a GCMAC adapted to its particular application.

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