Least Mean Square Learning in Associative Memory Networks
Least Mean Square Learning in Associative Memory Networks
This paper proposes and investigates theoretically the use of a class of neural networks called Associative Memory Networks for on-line adaptive nonlinear modelling and control. This class of networks is defined to include such algorithms as the CMAC, B-splines and Fuzzy Logic. Firstly, the algorithms are defined within a unifying framework which provides a natural decomposition for a parallel implementation. Then the modelling capabilities of the networks are investigated, and some new results about the CMAC are presented. Next the instantaneous learning rules are derived and investigated from a geometrical perspective, which allows the rate of convergence to be analysed. Also a measure of the learning interference for different set (basis function) shapes is obtained. Finally, an example of an Associative Memory Network guiding an autonomous vehicle into a slot is given.
531-536
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1992
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M. and Harris, C.J.
(1992)
Least Mean Square Learning in Associative Memory Networks.
Int. Symp. on Intelligent Control.
.
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Conference or Workshop Item
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Abstract
This paper proposes and investigates theoretically the use of a class of neural networks called Associative Memory Networks for on-line adaptive nonlinear modelling and control. This class of networks is defined to include such algorithms as the CMAC, B-splines and Fuzzy Logic. Firstly, the algorithms are defined within a unifying framework which provides a natural decomposition for a parallel implementation. Then the modelling capabilities of the networks are investigated, and some new results about the CMAC are presented. Next the instantaneous learning rules are derived and investigated from a geometrical perspective, which allows the rate of convergence to be analysed. Also a measure of the learning interference for different set (basis function) shapes is obtained. Finally, an example of an Associative Memory Network guiding an autonomous vehicle into a slot is given.
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Published date: 1992
Additional Information:
Organisation: IEEE Address: Glasgow, UK
Venue - Dates:
Int. Symp. on Intelligent Control, 1992-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250249
URI: http://eprints.soton.ac.uk/id/eprint/250249
PURE UUID: c9fbe05f-592b-4acc-932c-579c7301090f
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Contributors
Author:
M. Brown
Author:
C.J. Harris
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