A learning algorithm for applying synthesized stable dynamics to system identification

In this paper the models discussed by Cohen are extended by introducing an input term. This allows the resulting models to be utilized for system identification tasks. This approach gives a direct way to encode qualitative information such as attractor dimension into the model. We prove that this mo...

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Bibliographic Details
Published inNeural networks Vol. 11; no. 1; pp. 81 - 87
Main Authors Howse, James W., Abdallah, Chaouki T., Heileman, Gregory L.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 1998
Elsevier Science
Subjects
Applied sciences
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Learning algorithm
Neural networks
Stability
System identification
Learning algorithm
Stability
System identification
Neural network
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Summary:In this paper the models discussed by Cohen are extended by introducing an input term. This allows the resulting models to be utilized for system identification tasks. This approach gives a direct way to encode qualitative information such as attractor dimension into the model. We prove that this model is stable in the sense that a bounded input leads to a bounded state when a minor restriction is imposed on the Lyapunov function. By employing this stability result, we are able to find a learning algorithm which guarantees convergence to a set of parameters for which the error between the model trajectories and the desired trajectories vanishes.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0893-6080
1879-2782
DOI:10.1016/S0893-6080(97)00109-3