Connected Associative Memory Neural Network with Dynamical Threshold Function

Xin-Min HUANG  Yasumitsu MIYAZAKI  

IEICE TRANSACTIONS on Information and Systems   Vol.E75-D   No.1   pp.170-179
Publication Date: 1992/01/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Bio-Cybernetics
neuron computer,  neural network,  pattern recognition,  

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This paper presents a new connected associative memory neural network. In this network, a threshold function which has two dynamical parameters is introduced. After analyzing the dynamical behaviors and giving an upper bound of the memory capacity of the conventional connected associative memory neural network, it is demonstrated that these parameters play an important role in the recalling processes of the connected neural network. An approximate method of evaluationg their optimum values is given. Further, the optimum feedback stopping time of this network is discussed. Therefore, in our network, the recalling processes are ended at the optimum feedback stopping time whether a state energy has been local minimum or not. The simulations on computer show that the dynamical behaviors of our network are greatly improved. Even though the number of learned patterns is so large as the number of neurons, the statistical properties of the dynamical behaviors of our network are that the output series of recalling processes approach to the expected patterns on their initial inputs.