Parameter analysis for removing the local minima of combinatorial optimization problems by using the inverse function delayed neural network

Authors:
Yoshihiro Hayakawa;Koji Nakajima
Affiliations:
Laboratory for Brainware Systems, Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University, Sendai, Japan;Laboratory for Brainware Systems, Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University, Sendai, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Avoidance of the Permanent Oscillating State in the Inverse Function Delayed Neural Network

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

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Abstract

The Inverse function Delayed (ID) model is a novel neuron model derived from a macroscopic model which is attached to conventional network action. The special characteristic of the ID model is to have the negative resistance effect. Such a negative resistance can actively destabilize undesirable states, and we expect that the ID model can avoid the local minimum problems for solving the combinatorial optimization problem. In computer simulations, we have shown that the ID network can avoid the local minimum problem with a particular combinatorial optimization problem, and we have also shown the existence of an appropriate parameter for finding an optimal solution with high success rate experimentally. In this paper, we theoretically estimate appropriate network parameters to remove all local minimum states.