Study on Improved PCA-SVM Model for Water Demand Prediction

摘要：

　　construct an improved water demand prediction model for support vector machine (SVM) on the basis of principle components analysis (PCA) in order to improve the accuracy of water demand prediction and prediction efficiency.Analyze the principal components of all the index factors which affect water demand; eliminate redundant information between the indices,thus to reduce SVM input dimensions; besides,it also introduces genetic algorithm,solved the problem that the traditional SUV parameters cannot optimized dynamically.A simulated experiment proves that the predication accuracy of this model is higher than SVM,BP neural network; this model has higher generalization ability and is an effective model for predicting water demand.

关键词： Support vector machine Genetic algorithm Principle component analysis Water demand

作者: Xiang Hong Xue Xiao Feng Xue Lei XU

作者单位: College of Computer Engineering Jiangsu Teachers University of Technology ChangZhou,China

会议类型: 国际会议

会议名称: the 2012 International Conference on Manufacturing Engineering and Automation (2012年制造工程与自动化国际会议(ICMEA2012))

会议地点: 广州

会议语种:英文

页码: 1320-1324

在线出版日期: 2012-11-16（万方平台首次上网日期，不代表论文的发表时间）