Improved Differential Evolution Based BP Neural Network for Prediction of Groundwater Table

摘要：

Groundwater table often shows complex nonlinear characteristic. Back Propagation (BP) neural network is increasingly used to predict groundwater table. However man-made selecting the structure of BP neural network has blindness and expends much time, so differential evolution (DE) algorithm was adopted to automatically search BP neural network weight matrix and threshold matrix. In order to improve the convergence of DE algorithm, a chaotic sequence based on logistic map was introduced to self-adaptively adjust mutation factor. Furthermore, a self-adapting crossover probability factor was presented to improve the populations diversity and the ability of escaping from the local optimum. Study case shows that, compared with groundwater level prediction model based on traditional BP neural network, the new prediction model based on DE and BP neural network can greatly improve the convergence speed and prediction precision.

关键词： improved Differential Evolution algorithm Back Propagation linear crossover probability self-adaptive mutation factor groundwater table prediction model

作者: Jihong Qu Yuepeng Li Juan Zhou

作者单位: North China University of Water Conversancy and Hydroelectric Power Zhengzhou, 450011, China

会议类型: 国际会议

会议名称: 2010 Third International Symposium on Knowledge Acquisition and Modeling(第三届知识获取与建模国际研讨会 KAN 2010)

会议地点: 武汉

会议语种:英文

页码: 36-39

在线出版日期: 2010-10-20（万方平台首次上网日期，不代表论文的发表时间）