Brown, M. and Harris, C.J.
Least Mean Square Learning in Associative Memory Networks
At Int. Symp. on Intelligent Control.
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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.
Conference or Workshop Item
||Organisation: IEEE Address: Glasgow, UK
|Venue - Dates:
||Int. Symp. on Intelligent Control, 1992-01-01
||Southampton Wireless Group
||04 May 1999
||18 Apr 2017 00:23
|Further Information:||Google Scholar|
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