PARAMETERS DETERMINATION METHOD OF PHASE-SPACE RECONSTRUCTION BASED ON DIFFERENTIAL ENTROPY RATIO AND RBF NEURAL NETWORK简

Phase space reconstruction is the first step of recognizing the chaotic time series. On the basis of differential entropy ratio method, the embedding dimension mopt and time delay τ are op- ritual for the state space reconstruction could be determined. But they are not the optimal parameters accepted for prediction. This study proposes an improved method based on the differential entropy ratio and Radial Basis Function （RBF） neural network to estimate the embedding dimension rn and the time delay τ, which have both optimal characteristics of the state space reconstruction and the prediction. Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively, and both the prediction accuracy and prediction length are improved greatly.

Parameters determination method of phase-space reconstruction based on differential entropy ratio and RBF neural network

Phase space reconstruction is the first step of recognizing the chaotic time series. On the basis of differential entropy ratio method, the embedding dimension m _opt and time delay τ are optimal for the state space reconstruction could be determined. But they are not the optimal parameters accepted for prediction. This study proposes an improved method based on the differential entropy ratio and Radial Basis Function (RBF) neural network to estimate the embedding dimension m and the time delay τ, which have both optimal characteristics of the state space reconstruction and the prediction. Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively, and both the prediction accuracy and prediction length are improved greatly.