Neural networks for nonlinear programming
IEEE Transactions on Circuits and SystemsPublished 1 May 1988
Michael Peter Kennedy, Leon O. Chua
Citations1,111
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TL;DR
By considering the total cocontent function, the solution of the canonical non linear programming circuit is reconciled with the problem being modeled and it is shown how the circuit can be realized using a neural network, thereby extending the results of D.W. Tank and J.J. Hopefield to the general nonlinear programming problem.
Abstract
The dynamics of the modified canonical nonlinear programming circuit are studied and how to guarantee the stability of the network's solution. By considering the total cocontent function, the solution of the canonical nonlinear programming circuit is reconciled with the problem being modeled. In addition, it is shown how the circuit can be realized using a neural network, thereby extending the results of D.W. Tank and J.J. Hopefield (ibid., vol.CAS-33, p.533-41, May 1986) to the general nonlinear programming problem.>
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Computer ScienceEngineering
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