축합조건의 분석을 통한 Langevine 경쟁 학습 신경회로망의 대역 최소화 근사 해석과 필기체 숫자 인식에 관한 연구

A study of global minimization analaysis of Langevine competitive learning neural network based on constraction condition and its application to recognition for the handwritten numeral

In this paper, we present the global minimization condition by an informal analysis of the Langevine competitive learning neural network. From the viewpoint of the stochastic process, it is important that competitive learning guarantees an optimal solution for pattern recognition. By analysis of the Fokker-Plank equation for the proposed neural network, we show that if an energy function has a special pseudo-convexity, Langevine competitive learning can find the global minima. Experimental results for pattern recognition of handwritten numeral data indicate the superiority of the proposed algorithm.