IMPROVED NEURAL NETWORKS FOR LINEAR AND NONLINEAR PROGRAMMING
1991 ◽
Vol 02
(04)
◽
pp. 331-339
◽
Keyword(s):
A method for improving the performance of artificial neural networks for linear and nonlinear programming is presented. By analyzing the behavior of the conventional penalty function, the reason for the inherent degenerating accuracy is discovered. Based on this, a new combination penalty function is proposed which can ensure that the equilibrium point is acceptably close to the optimal point. A known neural network model has been modified by using the new penalty function and the corresponding circuit scheme is given. Simulation results show that the relative error for linear and nonlinear programming is substantially reduced by the new method.
1999 ◽
Vol 10
(6)
◽
pp. 1271-1290
◽
1985 ◽
Vol 67
(2)
◽
pp. 454-455
◽
2000 ◽
Vol 176
◽
pp. 135-136
2010 ◽
Vol 61
(2)
◽
pp. 120-124
◽