基于信息熵优化相空间重构参数的混沌时间序列预测

提出一种混沌时间序列相空间重构参数的信息熵优化方法(IEOP),该方法首先使用条件熵表示信息量,建立时间延迟和嵌入维数在相空间中的信息熵优化模型,然后利用遗传算法同时求解两个重构参数,使重构坐标间既保持了良好的独立性又保留了原系统的动力学特征.通过在Lorenz和Mackey-Glass系统上的数值实验,该方法不仅能够确定合适的嵌入维数和时间延迟,而且能在优化的相空间中获得更多的信息,提高了混沌时间序列的预测精度.

Chaotic time series prediction based on information entropy optimized parameters of phase space reconstruction

This paper proposes a method of information entropy optimized parameters (IEOP) of pahse space recon struction. First, it establishes an information entropy optimum model in phase space for embedding dimension and delay time by using conditional entropy. It then solves these two parameters with genetic algorithm (GA) simultaneously. IEOP constructs an optimum phase space, which maintains independence of reconstruction coordinate and retains the dynamic characteristics of the original system. In the numerical simulations, results of the Lorenz system and Mackey-Glass system show that it not only determines two parameters at the same time, but also can obtains more information in the optimized phase space, there by improving the performance of chaotic time series prediction.