一类改进的混沌时间序列局域非线性自适应预测

摘要

In order to improve the predictive performance for chaotic time series,we use a nonlinear function with a parameter λ to build a new adaptive prediction model.We evaluate the improved model using four well-known chaotic systems, namely Logistic map,Henon map,Lorenz system and Rosslor system.All the results show an increase in one-step predictive performance,comparing with the local nonlinear adaptive prediction model.And the improved model is also anti-noise to some extent.%为了提高混沌时间序列的预测性能,在局域非线性自适应预测模型的非线性函数中引入参数,λ通过选择合适的λ建立新的非线性预测模型。通过对Logistic混沌映射、Henon混沌映射、Lorenz混沌流和Rosslor混沌流进行仿真计算,结果表明该模型的预测精度比局域非线性自适应预测的一步预测精度高,且具有一定程度的抗噪性能。