BINARY NEURAL-NETWORK WITH NEGATIVE SELF-FEEDBACK AND ITS APPLICATIONTO N-QUEENS PROBLEM

This article deals with the binary neural network with negative self-f eedback connections as a method for solving combinatorial optimization problems. Although the binary neural network has a high convergence s peed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In this article, an improve ment using the negative self-feedback is proposed. First it is shown t hat the negative self-feedback can make some local minimums be unstabl e. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, thi s selection rule is effective to escape a local minimum. In order to c onfirm the effectiveness of this selection rule, some computer simulat ions are carried out for the N-Queens problem. For N = 256, the networ k is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).