For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Binary Neural Network with Negative Self-Feedback and Its Application to N-Queens Problem
Masaya OHTA Akio OGIHARA Kunio FUKUNAGA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1994/04/25
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Neurocomputing)
Category: Network Synthesis
binary neural network, negative self-feedback connection, selection rule, N-Queens problem,
Full Text: PDF>>
This article deals with the binary neural network with negative self-feedback connections as a method for solving combinational 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 thie 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 comfirm 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).