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

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.