A DCT domain quadratic predictor for real-time prediction of continuous chaotic signals
连续混沌信号的离散余弦变换域二次实时滤波预测

A DCT domain quadratic predictor with normalized least mean square (NLMS) algorithm, efficient implementing structure of the reduced parameter second-order Vloterra filter (RPSOVF), is proposed to investigate nonlinear real-time multi-step prediction performance of three kinds of continuous chaotic signals. Experimental results show that the real-time ahead one-step prediction mean square errors of the DCT domain quadratic predictor proposed in this paper are at least 100 times smaller than that of RPSOVF, which indicates that this predictor has better predictive performance, its structure is simple and easy to implement. Multi-step prediction performance of this DCT domain quadratic predictor to three continuous chaotic time series is superior to the one of local prediction method obviously, and its mean square errors do not increase exponentially according as the prediction step.