Least Mean Square Learning in Associative Memory Networks
Brown, M. and Harris, C.J. (1992) Least Mean Square Learning in Associative Memory Networks. Int. Symp. on Intelligent Control , 531-536.
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Description/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.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Additional Information: | Organisation: IEEE Address: Glasgow, UK |
| Divisions: | Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control |
| Item ID: | 250249 |
| Date Deposited: | 04 May 1999 |
| Last Modified: | 02 Mar 2012 13:39 |
| Contributors: | Brown, M. (Author) Harris, C.J. (Author) |
| Date: | 1992 |
| Additional Information: | Organisation: IEEE Address: Glasgow, UK |
| Status: | Published |
| Further Information: | Google Scholar |
| ISI Citation Count: | 3 |
| URI: | http://eprints.soton.ac.uk/id/eprint/250249 |
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