Radial basis function neural network predictor for parameter estimation in chaotic noise

Chaotic noise cancellation has potential application in both secret communication and radar target identification. To solve the problem of parameter estimation in chaotic noise, a novel radial basis function neural network (RBF-NN) - based chaotic time series data modeling method is presented in this paper. Together with the spectral analysis technique, the algorithm combines neural network's ability to approximate any nonlinear function. Based on the flexibility of RBF-NN predictor and classical amplitude spectral analysis technique, this paper proposes a new algorithm for parameter estimation in chaotic noise. Analysis of the proposed algorithm's principle and simulation experiments results are given out, which show the effective of the proposed method. We conclude that the study has potential application in various fields as in secret communication for narrow band interference rejection or attenuation and in radar signal processing for weak target detection and identification in sea clutter.