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Neural networks for constrained optimization problems
Author(s) -
Lillo Walter E.,
Hui Stefen,
Żak Stanislaw H.
Publication year - 1993
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.4490210408
Subject(s) - artificial neural network , computer science , mathematical optimization , norm (philosophy) , class (philosophy) , stochastic neural network , focus (optics) , linear programming , constrained optimization , optimal control , time delay neural network , algorithm , mathematics , artificial intelligence , physics , optics , political science , law
This paper is concerned with utilizing neural networks and analogue circuits to solve constrained optimization problems. We propose a novel neural network architecture for solving a class of non‐linear programming problems. the proposed neural network is then used, and if necessary modified, to solve minimum norm problems subject to linear constraints. Minimum norm problems have many applications in various areas, but we focus on their applications to the control of discrete dynamic processes. the applicability of the proposed neural network is demonstrated on numerical examples.