A new proposed method, i.e. the recurrent neural network (RNN), is introduced to predict chaotic time series. The effectiveness of using RNN for making one-step and multi-step predictions is tested based on remarkable...A new proposed method, i.e. the recurrent neural network (RNN), is introduced to predict chaotic time series. The effectiveness of using RNN for making one-step and multi-step predictions is tested based on remarkable few datum points by computer-generated chaotic time series. Numerical results show that the RNN proposed here is a very powerful tool for making prediction of chaotic time series.展开更多
A newly proposed method,i.e.the adaptive higher-order nonlinear finite impulse response(HONFIR)filter based on higher-order sparse Volterra series expansions,is introduced to predict hyper-chaotic time series.The effe...A newly proposed method,i.e.the adaptive higher-order nonlinear finite impulse response(HONFIR)filter based on higher-order sparse Volterra series expansions,is introduced to predict hyper-chaotic time series.The effectiveness of using adaptive HONFIR filter for making one-step and multi-step predictions is tested based on very few data points by computer-generated hyper-chaotic time series including Mackey-Glass equation and 4-dimensional nonlinear dynamical system.A comparison is made with some neural networks for predicting the Mackey-Glass hyper-chaotic time series.Numerical simulation results show that the adaptive HONFIR filter proposed here is a very powerful tool for making prediction of hyper-chaotic time series.展开更多
基金Supported by National Defense Foundation of China Under Grant Nos.98JS05.4.1.DZ0205.
文摘A new proposed method, i.e. the recurrent neural network (RNN), is introduced to predict chaotic time series. The effectiveness of using RNN for making one-step and multi-step predictions is tested based on remarkable few datum points by computer-generated chaotic time series. Numerical results show that the RNN proposed here is a very powerful tool for making prediction of chaotic time series.
基金Supported by the National Defense Foundation of China under Grant No.98JS05.4.1.DZ0205.
文摘A newly proposed method,i.e.the adaptive higher-order nonlinear finite impulse response(HONFIR)filter based on higher-order sparse Volterra series expansions,is introduced to predict hyper-chaotic time series.The effectiveness of using adaptive HONFIR filter for making one-step and multi-step predictions is tested based on very few data points by computer-generated hyper-chaotic time series including Mackey-Glass equation and 4-dimensional nonlinear dynamical system.A comparison is made with some neural networks for predicting the Mackey-Glass hyper-chaotic time series.Numerical simulation results show that the adaptive HONFIR filter proposed here is a very powerful tool for making prediction of hyper-chaotic time series.
