基于均匀设计优化预测模型参数的混沌时间序列预测  

CHAOTIC TIME SERIES PREDICTION BASED ON OPTIMISING PREDICTION PARAMETERS MODEL WITH UNIFORM DESIGN

作　　者：陈海英[1] 吴明慧[2] 

机构地区：[1]华中农业大学楚天学院,湖北武汉430205 [2]信阳职业技术学院,河南信阳464000

出　　处：《计算机应用与软件》2014年第10期176-179,共4页Computer Applications and Software

基　　金：湖北省高校省级教学研究项目(2012458);2014年校级科研项目(201405)

摘　　要：为解决混沌时间序列预测中的延迟时间、嵌入维与模型参数等优化问题,提出一种基于均匀设计优化预测模型参数的混沌时间序列预测模型(UD-LSSVM)。首先采用均匀设计产生多个参数组合,并采用最小二乘支持向量机得到每组参数的均方根误差(RMSE);然后最小二乘支持向量机对参数进行全组合寻优建立最优混沌时间预测模型;最后进行混沌时间序列仿真测试。仿真结果表明,相对于对比模型,UD-LSSVM不仅可以快速、准确找到延迟时间、嵌入维与模型参数的最优组合,而且提高了混沌时间序列预测的预测精度。In order to solve the optimisation problems of delay time, dimension embedding and model parameters in chaotic time series prediction, we propose a prediction model of chaotic time series which is based on optimising prediction model parameters with uniform design. First we use uniform design to produce multiple parameter combinations, and use least square service vector machine （LSSVM） to obtain the root mean square error （RMSE） of every group of parameters. Secondly, we use LSSVM to conduct full combination optimisation on parameters to build the optimal chaotic time prediction model. Finally, the simulation experiments are carried out on chaotic time series. Simulation result illustrates that in comparison with contrasting models, the proposed model can quickly and accurately find the optimal combination of delay time, dimension embedding and model parameters, and the prediction accurate of chaotic time series is improved as well.

关 键 词：均匀设计 支持向量机 参数优化 混沌时间序列 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]