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13.395 Articles in journals published by the UPC
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  • 1997, Vol. IV, Núm. 3
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Neural networks learning as a multiobjective optimal control problem

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Krawczak, Maciej
Document typeArticle
Defense date1997
PublisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
The supervised learning process of multilayer feedforward neural networks can be considered as a class of multi-objective, multi-stage optimal control problem. An iterative parametric minimax method is proposed in which the original optimization problem is embedded into a weighted minimax formulation. The resulting auxiliary parametric optimization problems at the lower level have simple structures that are readily tackled by efficient solution methods, such as the dynamic programming or the error backpropagation algorithm. The analytical expression of the partial derivatives of systems performance indices with respect to the weighting vector in the parametric minimax formulation is derived.
URIhttp://hdl.handle.net/2099/3498
ISSN1134-5632
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  • Mathware & soft computing - 1997, Vol. IV, Núm. 3 [10]
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