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Basis function models of the CMAC network.

A Kolcz¹, Nigel M. Allinson

¹ECE Department, University of Colorado at Colorado Springs, 1420 Austin Bluffs Pkwy., Colorado Springs, USA

Neural Networks : the Official Journal of the International Neural Network Society

|March 29, 2003

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The Cerebellar Model Articulation Controller (CMAC) is interpreted as a General Memory Neural Network (GMNN). This provides a new basis function model for CMAC, offering insights into its performance and mapping capabilities.

Area of Science:

Artificial Intelligence
Computational Neuroscience
Machine Learning

Background:

The Cerebellar Model Articulation Controller (CMAC) is a widely used neural network architecture.
Existing interpretations often focus on its direct application rather than its foundational structure.
Understanding CMAC within broader neural network frameworks can enhance its theoretical basis.

Purpose of the Study:

To interpret the Cerebellar Model Articulation Controller (CMAC) network within the General Memory Neural Network (GMNN) architecture.
To present CMAC as a specific type of basis function network.
To derive an average basis function form for CMAC based on its input quantization.

Main Methods:

Interpreting CMAC within the General Memory Neural Network (GMNN) formalism.

Related Experiment Videos

Analyzing CMAC's input quantization to define its basis function characteristics.

Developing basis-function models to represent CMAC mappings.

Utilizing numerical simulations to support the theoretical developments.

Main Results:

CMAC is successfully framed as a specific instance of a basis function network within the GMNN architecture.
A generalized, average basis function form for CMAC was derived.
The derived basis form enables the creation of basis-function models for CMAC.
These models offer enhanced insight into CMAC's mapping properties and performance.

Conclusions:

The GMNN framework provides a valuable perspective for understanding CMAC.
The derived average basis function offers a new tool for analyzing and modeling CMAC.
This approach deepens the theoretical understanding of CMAC's internal workings and predictive capabilities.