Neural net algorithms for dynamical systems

Cheung, Y.M., 1992. Neural net algorithms for dynamical systems. PhD, Nottingham Trent University.

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A neural net based algorithm is devised as an alternative to traditional analogue/numerical integration. The new algorithm consists of a multilayered neural net integrator model inspired by the neuron organisation of the vertebrate retina. A mixture of implicit weight setting, supervised and unsupervised learning is employed. The convergence of this approach proves to be fast when compared to existing models producing comparable results.

When the model is operating in a closed loop system it yields a consistent estimate of the derivatives of pictorial input profiles.

The mapping of the resulting neural net models onto single and multiprocessor systems is examined. A general framework is formulated to permit arbitrary network, definition and easy alterations of network parameters.

A parallel processing technique for distributed memory multiprocessor systems is devised. The parallel algorithm yields a large reduction in processing time.

Item Type: Thesis
Creators: Cheung, Y.M.
Date: 1992
ISBN: 9781369324471
Rights: This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognize that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without the author's prior written consent.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 12 Nov 2020 14:11
Last Modified: 12 Oct 2023 10:37

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