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Correlating matched-filter model for analysis and optimisation of neural networks

Selviah, D.R.; Midwinter, J.E.; Rivers, A.W.; Lung, K.W.; (1989) Correlating matched-filter model for analysis and optimisation of neural networks. IEE Proceedings F: Radar and Signal Processing , 136 (3) pp.143 - 148. Green open access

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Abstract

A new formalism is described for modelling neural networks by means of which a clear physical understanding of the network behaviour can be gained. In essence, the neural net is represented by an equivalent network of matched filters which is then analysed by standard correlation techniques. The procedure is demonstrated on the synchronous Little-Hopfield network. It is shown how the ability of this network to discriminate between stored binary, bipolar codes is optimised if the stored codes are chosen to be orthogonal. However, such a choice will not often be possible and so a new neural network architecture is proposed which enables the same discrimination to be obtained for arbitrary stored codes. The most efficient convergence of the synchronous Little-Hopfield net is obtained when the neurons are connected to themselves with a weight equal to the number of stored codes. The processing gain is presented for this case. The paper goes on to show how this modelling technique can be extended to analyse the behaviour of both hard and soft neural threshold responses and a novel time-dependent threshold response is described.

Type: Article
Title: Correlating matched-filter model for analysis and optimisation of neural networks
Open access status: An open access version is available from UCL Discovery
Publisher version: http://ieeexplore.ieee.org/iel5/2209/5665/00216603...
Language: English
Additional information: ©1989 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: content-addressable storage, correlation methods, matched filters, neural nets
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/2666
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