A high order feedback net (HOFNET) with variable non-linearity.
Second International Conference on Artificial Neural Networks, 1991.
(pp. pp.59 - 63).
IEEE: London, UK.
Most neural networks proposed for pattern recognition sample the incoming image at one instant and then analyse it. This means that the data to be analysed is limited to that containing the noise present at one instant. Time independent noise is therefore, captured but only one sample of time dependent noise is included in the analysis. If however, the incoming image is sampled at several instants, or continuously, then in the subsequent analysis the time dependent noise can be averaged out. This, of course, assumes that sufficient samples can be taken before the object being imaged, has moved an appreciable distance in the field of view. High speed sampling requires parallel image input and is most conveniently carried out by optoelectronic neural network image analysis systems. Optical technology is particularly good at performing certain operations, such as Fourier Transforms, correlations and convolutions while others such as subtraction are difficult. So for an optical net it is best to choose an architecture based on convenient operations such as the high order neural networks.
|Title:||A high order feedback net (HOFNET) with variable non-linearity|
|Open access status:||An open access version is available from UCL Discovery|
|Additional information:||©1991 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:||feedback, neural nets, optical information processing, pattern recognition|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Electronic and Electrical Engineering|
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