Learning Model and Application of New Preceding Layer Driven MLP Neural Network

새로운 Preceding Layer Driven MLP 신경회로망의 학습 모델과 그 응용

  • 한효진 (경북대학교 전자공학과) ;
  • 김동훈 (경북대학교 전자공학과) ;
  • 정호선 (경북대학교 전자공학과)
  • Published : 1991.12.01

Abstract

In this paper, the novel PLD (Preceding Layer Driven) MLP (Multi Layer Perceptron) neural network model and its learning algorithm is described. This learning algorithm is different from the conventional. This integer weights and hard limit function are used for synaptic weight values and activation function, respectively. The entire learning process is performed by layer-by-layer method. the number of layers can be varied with difficulty of training data. Since the synaptic weight values are integers, the synapse circuit can be easily implemented with CMOS. PLD MLP neural network was applied to English Characters, arbitrary waveform generation and spiral problem.

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