Binary Neural Network with Negative Self-Feedback and Its Application to N-Queens Problem (Special Issue on Neurocomputing)

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This article deals with the binary neural network with negative self-feedback connections as a method for solving combinatorial optimization problems. Although the binary neural network has a high convergence speed, it hardly searches out the optimum solution, because the neuron is selected randomly at each state update. In this article, an improvement using the negative self-feedback is proposed. First it is shown that the negative self-feedback can make some local minimums be unstable. Second a selection rule is proposed and its property is analyzed in detail. In the binary neural network with negative self-feedback, this selection rule is effective to escape a local minimum. In order to confirm the effectiveness of this selection rule, some computer simulations are carried out for the N-Queens problem. For N=256, the network is not caught in any local minimum and provides the optimum solution within 2654 steps (about 10 minutes).
社団法人電子情報通信学会の論文
1994-04-25