Fourier analysis of the generalized CMAC neural network
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.
cmac neural networks associative memory function approximation modeling capabilities