RESULTANT PROJECTION NEURAL NETWORKS FOR OPTIMIZATION UNDER INEQUALITY CONSTRAINTS

In this paper we propose Resultant Projection Neural Networks, based o n the idea of orthogonal projections onto convex sets for solving opti mization problems under inequality constraints, The proposed network i s capable of solving optimization problems with inequality constraints which cannot be solved directly using a Hopfield network, The effect of various network parameters on the optimization process are theoreti cally analyzed, A probabilistic analysis of the expected performance o f the network has been carried out for the 0-1 knapsack problem, Simul ation results for the 0-1 knapsack, multidimensional 0-1 knapsack and job processing with deadlines are also shown, The average performance (mean and median) of the network compare quite well with optimal and s uboptimal solutions obtained using standard techniques in conventional computers, However, there are some instances which do produce bad sol utions.