Performance improvement of single-layer neural network with feedback by analyzing the computational energy function

계산 에너지 함수 분석을 통한 궤환성을 갖는 단층신경회로망의 성능개선

A new method to neglect the third term of the computational energy expression in the single-layer neural network with feedback is introduced. The system often converges to local minima instead of to global minima, because the computational energy is not matched exactly with the cost function being optimized. One of the factors causing these tow functions different is the third term of computational enegy expression. Regarding this third term energy very small, it is always ignored in designing the system. However, a sthe system growing, this third term energy is also growing and this grown term makes the computational energy function much different from the cost function. In results of differency between two functions, system converges to local minima more than before. In this paper, a new method to neglect te third term energy is introduced, so that the system with tis new method has been imroved.