ニューラルネットによる最適化問題の一解法

This article consider an optimaization problems solver using a neural network. Tank and Hopfield have proposed a linear programming solver with a neural network. The network can seek a minimum of an energy function, but they did not prove that this minimum corresponds to the solution of the problems. In this article, we popopose a novel synthesis procedure which can seek a minimum of a cost function for an optimization problems. Our system does not define the energy function, then the system may have an oscillating state. However, this system guantees that a fixed point corresponds to a minimum of a cost function.