Learning of the Coulomb energy network on the variation of the temperature

Abstract

A method that minimizes the energy function on the variation not only of weight but also of temperature for the Coulomb energy network (CEN) is proposed. The proposed method is compared with the traditional learning method using only weight variation. It is shown that learning is done more efficiently and accurately with the proposed method. Since weight and temperature can be learned in parallel, the speed of learning might be doubled if appropriate hardware support is provided. The concept of the distance is used to solve the linearly nonseparable classification problem, which cannot be solved in the traditional supervised CEN.<>