A Method of Improving Generalization Ability for Neural Network Based on Genetic Algorithm

摘要：

In order to solve the problem that neural network learns well but predicts badly, the genetic algorithm was adopted to optimize the neural network. The LM-BP neural network learns very well, and it is sensitive to the initial weights and thresholds. Then its initial weights and thresholds were selected by genetic algorithm. So the method of improving generalization ability for neural network based on genetic algorithm was proposed. By example analysis, compared with the method that the initial weights and thresholds were selected randomly, the neural network optimized by genetic algorithm has very high fitting precision and testing accuracy. The new method can greatly improve the generalization ability of neural network.

关键词： neural network genetic optimized algorithm fating precision testing accuracy

作者: GUO hai-ru LI Zhi-min

作者单位: School of Computer and Information Science Xiaogan University Xiaogan, China

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 742-745

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）