基于遗忘进化规划的Hopfield网学习算法

A Learning Algorithm of Hopfield Neural Network Based on Evolutionary Programming with Forgetting

本文提出了一个基于遗忘进化规划的Ｈｏｐｆｉｅｌｄ网学习算法．通过遗忘部分个体，算法能避免局部最小．给定不动点、极限环或迭代序列，通过解不等式，算法能同时获得Ｈｏｐｆｉｅｌｄ网的拓扑结构和权值．该算法克服了进化Ｈｏｐｆｉｅｌｄ网学习的局限性．它还能找到多个优化解．实验也证明了该算法的有效性．

This paper presents a learning algorithm of Hopfield neural network based on evolutionary programming with forgetting. The algorithm can avoid local minima by forgetting some individuals. Under constraints of fixed points, limit cycles or iteration sequences, the algorithm simultaneously acquires both the topology and weights for Hopfield neural network by solving inequalities. It copes with the limitations of evolving Hopfield learning algorithm. It can also find several optimal solutions. The experimental results also demonstrate the effectiveness of the algorithm.