基于极大极小准则的异联想记忆网络学习算法

Learning Algorithms ot Bidirectional Associative Memory Based on Max-Min Criterion

提出了设计异联想记忆网络的极大极小准则，即设计出的连接权阵应使得网络最小的记忆模式对的吸引域达到最大．首先给出了一种快速学习算法，它设计出的网络连接权值只取１，０或－１；再进一步发展了一个启发性迭代学习算法，称为约束感知器优化学习算法，它以快速学习算法的结果作为连接权阵的迭代初值．计算机实验结果表明了所提学习算法的优越性．

A max-min criterion for design of bidirectional associative memory, which requires the smallest domain of attraction to be maximized, is proposed in this paper. A quick learning algorithm is first given, by which the designed connection weights are 1,0 or -1. Further, a constrained perceptron optimization algorithm is presented, which takes the weights obtained by quick algorithm as initial iteration value. Computer experimental results confirm the advantages of the proposed algorithms.