Publication: Design and analysis of an efficient neural network model for solving nonlinear optimization problems
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Date
2005-10-20
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
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Volume Title
Publisher
Taylor & Francis Ltd
Type
Article
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Acesso restrito
Abstract
This paper presents an efficient approach based on a recurrent neural network for solving constrained nonlinear optimization. More specifically, a modified Hopfield network is developed, and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it handles optimization and constraint terms in different stages with no interference from each other. Moreover, the proposed approach does not require specification for penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyse its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network.
Description
Language
English
Citation
International Journal of Systems Science. Abingdon: Taylor & Francis Ltd, v. 36, n. 13, p. 833-843, 2005.
