High-dimensional time delays selection for phase space reconstruction with information theory

摘要

A method of information entropy optimized time delays is proposed for the chaotic time series reconstruction. First, it establishes an information entropy optimum model in phase space for high-dimensional time delays by using conditional entropy. Then solved these parameters using genetic algorithm(GA). This method 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 show that it could improve the performance of chaotic time series prediction.