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Least Mean Square Learning in Associative Memory Networks

Brown, M. and Harris, C.J. (1992) Least Mean Square Learning in Associative Memory Networks At Int. Symp. on Intelligent Control. , pp. 531-536.

Record type: Conference or Workshop Item (Other)


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


Local EPrints ID: 250249
PURE UUID: c9fbe05f-592b-4acc-932c-579c7301090f

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

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Author: M. Brown
Author: C.J. Harris

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