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Start Over Subject constrained optimization

1. On solving nonlinear programming problems via neural networks

Creator:
Mladenov, V. M., Maratos, N. G., Tsakoumis, A. C., Tashev, T. A., and Mastorakis, N. E.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
neural networks, nonlinear programming, constrained optimization, convex programming, penalty functions, and exact penalty functions
Language:
English
Description:
In this paper we consider several Neural Network architectures for solving nonlinear programming problems with inequality constrains. This is an extension of previous authors’ work and here we present a new architecture for convex programming problems. The architecture is based on alternativě pseudocost function, which do not require large penalty pararneter values. Simulation results based on SIMULINK® models are given and compared.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public