An Informal Analysis of Diffusion, Global Optimization Properties in Langevine Competitive Learning Neural Network

Langevine 경쟁학습 신경회로망의 확산성과 대역 최적화 성질의 근사 해석

In this paper, we discuss an informal analysis of diffusion, global optimization properties of Langevine competitive learning neural network. In the view of the stochastic process, it is important that competitive learning gurantee an optimal solution for pattern recognition. We show that the binary reinforcement function in Langevine competitive learning is a brownian motion as Gaussian process, and construct the Fokker-Plank equation for the proposed neural network. Finally, we show that the informal analysis of the proposed algorithm has a possiblity of globally optimal. solution with the proper initial condition.