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Associative Memory Network Construction Algorithms

Associative Memory Network Construction Algorithms
Associative Memory Network Construction Algorithms
In this paper a variety of Associative Memory Network (AMN) construction algorithms are briefly described and explained, namely the Multivariate Adaptive Regression Splines (MARS) algorithm of Friedman, and the Adaptive Spline Modelling of Observation Data (ASMOD) and Adaptive B-spline Basis function Modelling of Observation Data (ABBMOD) algorithms of Kavli. Such algorithms may be used for high-dimensional functional approximation in problems where there exits redundancy in the input data such that an accurate approximation may be formed additively from low order models. Since the process of model formation is automated, the algorithms may also be used for automatic model initialisation in some adaptive identification schemes. Since both the ASMOD and ABBMOD algorithms use B-spline basis functions which may be interpreted as a set of linguistic rules via fuzzy calculus, these algorithms may be used in the automatic generation of fuzzy rule bases.
Bridgett, N.A.
25b96061-a19f-46cf-bef4-b63b56fb5fe1
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Mills, D.J.
bd207c8b-fbf0-41da-bba4-b54d9a29804d
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Bridgett, N.A.
25b96061-a19f-46cf-bef4-b63b56fb5fe1
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Mills, D.J.
bd207c8b-fbf0-41da-bba4-b54d9a29804d
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Bridgett, N.A., Brown, M., Mills, D.J. and Harris, C.J. (1994) Associative Memory Network Construction Algorithms. Int. Symp. on Signal Processing, Robotics And Neural Networks.

Record type: Conference or Workshop Item (Other)

Abstract

In this paper a variety of Associative Memory Network (AMN) construction algorithms are briefly described and explained, namely the Multivariate Adaptive Regression Splines (MARS) algorithm of Friedman, and the Adaptive Spline Modelling of Observation Data (ASMOD) and Adaptive B-spline Basis function Modelling of Observation Data (ABBMOD) algorithms of Kavli. Such algorithms may be used for high-dimensional functional approximation in problems where there exits redundancy in the input data such that an accurate approximation may be formed additively from low order models. Since the process of model formation is automated, the algorithms may also be used for automatic model initialisation in some adaptive identification schemes. Since both the ASMOD and ABBMOD algorithms use B-spline basis functions which may be interpreted as a set of linguistic rules via fuzzy calculus, these algorithms may be used in the automatic generation of fuzzy rule bases.

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More information

Published date: 1994
Additional Information: Organisation: IMACS Address: Lille, France
Venue - Dates: Int. Symp. on Signal Processing, Robotics And Neural Networks, 1994-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250236
URI: http://eprints.soton.ac.uk/id/eprint/250236
PURE UUID: 4d9d30c6-af77-47ec-a988-6e28b6248243

Catalogue record

Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07

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Contributors

Author: N.A. Bridgett
Author: M. Brown
Author: D.J. Mills
Author: C.J. Harris

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