DocumentCode
2444952
Title
On solving constrained optimization problems with neural networks
Author
Glazos, M.P. ; Hui, Stefen ; Zak, Stanislaw H.
Author_Institution
Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA
Volume
7
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
4547
Abstract
We analyze a class of neural networks that solve convex programming problems. In carrying out the analysis we use concepts from the theory of differential equations with discontinuous right-hand sides and Lyapunov stability theory. We show that irrespective of the initial state of the network the state converges to a solution of the convex programming problem. The dynamic behavior of the networks is illustrated by two numerical examples
Keywords
Lyapunov methods; convex programming; differential equations; neural nets; nonlinear programming; optimisation; Lyapunov stability theory; constrained optimization; convex programming; differential equations; neural networks; state converges; Constraint optimization; Design optimization; Differential equations; Intelligent networks; Lyapunov method; Mathematical programming; Neural networks; Switched capacitor networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.375006
Filename
375006
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