非线性混沌时序预测研究及应用

混沌是一种普遍存在的非线性动力学行为，针对混沌时间序列的难以预测和控制的问题，提出了基于趋势的混沌预测模型，利用混沌系统的初值、参数敏感性来微调和控制系统扰动。并用改进的最优化方法来估计模型的参数，在其相空间中对时序的未来值进行预测，算例表明。选取最佳的模型阶数能增加预测的准确程度，它不仅克服了仅用延迟嵌入技术的弊端，也降低了直接使用预测误差决定输入模式的盲目性．预测效果比其它时序方法要好．

Research on Optimize Prediction Model and Algorithm about Not Line Chaotic Time Series

The chaotic was a kind of widespread not line dynamics behavior, and it was harmful under majority circumstance. Facing the problem of difficulty to estimate and control chaotic time series, we putted forward a chaotic estimating model. by using the parameter of the chaotic system, sensitivity of the parameter to inching and control the disturbance of the system , and estimated the parameter of the model by using the best update option. In the end, we forecast the intending series value in its mutually space. The example shows that it can increase the precision in the estimated process by selecting the best model steps. It not only conquer the abuse of using detention inlay technology alone, but also decrease blindness of using forecast error to decide the input model directly, and the result of it is better than the method of statistics and other series means.