A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented. 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 t...

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Bibliographic Details
Published inProceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks pp. 213 - 218
Main Authors Nunes da Silva, I., Nunes de Souza, A., Bordon, M.E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Artificial neural networks
Biological system modeling
Computational modeling
Computer networks
Constraint optimization
Design optimization
Equations
Fuzzy logic
Neural networks
Neurons
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Summary:A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented. 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. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.
ISBN:9780769508566
0769508561
ISSN:1522-4899
2375-0235
DOI:10.1109/SBRN.2000.889741