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Lookup NU author(s): Dr Will Woods, Sam Johnson, Andrew Batchelor, Dr Gary Green
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Time-Delayed Neural Networks (TDNNs) can be used to learn the dynamics of an unknown system from input-output data. In many cases a model of the system is also available in the form of a system of ODEs, derived either from first principles or using heuristic arguments. In such cases a functional comparison can be made between the dynamic behaviour of the model with that of the trained TDNN for arbitrary inputs. We show that Volterra kernels for both the model system and the TDNN can be obtained, and thus the system responses compared, in a manner that is independent of the input. The techniques of Structural Bilinearisation of ODEs and Volterra series expansion of a TDNN, are demonstrated by application to the Hodgkin-Huxley set of equations.
Author(s): Woods WP, Johnson SR, Batchelor AH, Green GGR
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Joint Conference on Neural Networks
Year of Conference: 2002
Pages: 1120-1125
Publisher: IEEE
URL: http://dx.doi.org/10.1109/IJCNN.2002.1007651
DOI: 10.1109/IJCNN.2002.1007651
Library holdings: Search Newcastle University Library for this item
ISBN: 0780372786
