Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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The dynamics of self-organizing feature map with constant learning rate and binary reinforcement function

시불변 학습계수와 이진 강화 함수를 가진 자기 조직화 형상지도 신경회로망의 동적특성

  • Seok, Jin-Uk (Dept. of Electronic and Electrical Engineering, Hongik University) ;
  • Jo, Seong-Won (Dept. of Electronic and Electrical Engineering, Hongik University)
  • 석진욱 (홍익대학교 공과대학 전자.전기공학군) ;
  • 조성원 (홍익대학교 공과대학 전자.전기공학군)
  • Published : 1996.06.01
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Abstract

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

Keywords

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