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On solving constrained optimization problems with neural networks

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3 Author(s)
M. P. Glazos ; Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA ; S. Hui ; S. H. Zak

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

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994