Comparative Aspects of Associative Memory Networks for Modelling
Comparative Aspects of Associative Memory Networks for Modelling
This paper will describe a class of networks called Associative Memory Networks which have many desirable properties for applications within the field of Intelligent Control. This class is defined to include the Albus CMAC neural network, the B-spline neural network and a certain class of Fuzzy Logic networks. These networks will first be described within a common framework which has a natural parallel implementation and then several learning rules will be derived. These are instantaneous gradient descent and error correction adaptive strategies and the sparse internal representation of the networks make them particularly suited to these learning rules. Finally all three networks will be applied to the same nonlinear time series prediction problem, comparing the strengths and weaknesses of each network.
454-459
An, P.E.
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Brown, M.
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Harris, C.J.
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Lawrence, A.J.
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Moore, C.G.
79001bdf-4225-447b-bbe8-cf81c1711906
1993
An, P.E.
5dc94657-d009-4d13-9a0f-6645a9d296d9
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Lawrence, A.J.
039e4bf7-a3bf-4650-b125-ab4fedbeec46
Moore, C.G.
79001bdf-4225-447b-bbe8-cf81c1711906
An, P.E., Brown, M., Harris, C.J., Lawrence, A.J. and Moore, C.G.
(1993)
Comparative Aspects of Associative Memory Networks for Modelling.
2nd European Control conference.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
This paper will describe a class of networks called Associative Memory Networks which have many desirable properties for applications within the field of Intelligent Control. This class is defined to include the Albus CMAC neural network, the B-spline neural network and a certain class of Fuzzy Logic networks. These networks will first be described within a common framework which has a natural parallel implementation and then several learning rules will be derived. These are instantaneous gradient descent and error correction adaptive strategies and the sparse internal representation of the networks make them particularly suited to these learning rules. Finally all three networks will be applied to the same nonlinear time series prediction problem, comparing the strengths and weaknesses of each network.
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More information
Published date: 1993
Additional Information:
Address: Groningen, Netherlands
Venue - Dates:
2nd European Control conference, 1993-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250214
URI: http://eprints.soton.ac.uk/id/eprint/250214
PURE UUID: 2d44b18a-d479-41a2-bb8e-15e8b6539ed5
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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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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