中南民族大学学报：自然科学版2011，Vol.30Issue(4)：98-101,4.
基于云自适应遗传算法的改进BP算法
An Improved BP Algorithm Based on Cloud Self-Adaptive Genetic Algorithm
摘要
Abstract
The standard BP algorithm is sensitive to the initial weights, converges slowly and is easy to trap into local minimum. Aiming at these limitations of the standard BP algorithm, combining the randomness and stability of the cloud droplets in the normal cloud model, and the global search ability and fast convergence of the genetic algorithm, the cloud self-adaptive genetic BP algorithm is put forward in this paper. This algorithm firstly combines the cloud model and the genetic algorithm to adjust the weights and threshold values of the neural network. The improved self-adaptive crossover probability and mutation probability are generated by X-conditional cloud generator. The results of the experiment show that the convergence speed of the cloud self-adaptive genetic BP algorithm is faster than that of the standard BP algorithm.关键词
云模型/遗传算法/云自适应遗传BP算法/神经网络Key words
cloud model/genetic algorithm/cloud self-adaptive genetic back propogation algorithm/neural network分类
信息技术与安全科学引用本文复制引用
吴立锋,程林辉..基于云自适应遗传算法的改进BP算法[J].中南民族大学学报：自然科学版,2011,30(4):98-101,4.基金项目
中南民族大学自然科学基金资助项目 （）
