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Associative Memory Networks: Adaptive Modelling Theory, Software Implementation and Graphical User Interface

An, P.E., Brown, M., Harris, C.J., Lawrence, A.J. and Moore, C.G. (1994) Associative Memory Networks: Adaptive Modelling Theory, Software Implementation and Graphical User Interface Engineering Applications in Artificial Intelligence, 7, (1), 1--21.

Record type: Article

Abstract

This paper describes in a unified mathematical framework a class of associative memory neural networks (AMN), that have very fast learning rates, local generalisation, parallel implementation, and guaranteed convergence to the mean squared error, making them appropriate for applications such as intelligent control and on-line modelling of nonlinear dynamical processes. The class of AMN considered include the Albus CMAC, B-Splines neural network and classes of fuzzy logic networks. Appropriate instantaneous learning rules are derived and applied to a benchmark nonlinear time series prediction problem. For practical implementation, a network software library and graphical user interface (GUI) is introduced for these networks. The data structure is modular, allowing a natural implementation on a parallel machine. The GUI provides a front end, for high-level procedures, allowing the networks to be designed, trained and analysed within a common environment with a minimum of user effort. The software library is readily integrable into industrial packages such as MATLAB.

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Published date: 1994
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250207
URI: http://eprints.soton.ac.uk/id/eprint/250207
PURE UUID: 1b86b185-65e6-42ea-8054-05e8a62e6319

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Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

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Contributors

Author: P.E. An
Author: M. Brown
Author: C.J. Harris
Author: A.J. Lawrence
Author: C.G. Moore

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