Title :
Neural networks for nonlinear programming
Author :
Kennedy, Michael Peter ; Chua, Leon O.
Author_Institution :
Dept. of Electr. & Comput. Sci., California Univ., Berkeley, CA, USA
fDate :
5/1/1988 12:00:00 AM
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
Keywords :
analogue computer circuits; neural nets; nonlinear programming; canonical nonlinear programming circuit; neural network; stability; total cocontent function; Circuit stability; Cost function; Dynamic programming; Functional programming; Hopfield neural networks; Linear programming; Neural networks; Neurons; Performance analysis; Transformers;
Journal_Title :
Circuits and Systems, IEEE Transactions on
