混沌背景中微弱信号检测的神经网络方法

基于复杂非线性系统相空间重构理论，提出了混沌背景中微弱信号检测的神经网络方法，利用神经网络强大的学习和非线性处理能力，建立了混沌背景噪声的一步预测模型，从预测误差中检测淹没在混沌背景噪声中的微弱目标信号(包括周期信号和瞬态信号)，研究了混沌背景中存在白噪声时该方法的检测能力，指出了目标信号为瞬态信号和周期信号时检测原理的异同点，最后以Lorenz系统作为混沌背景噪声进行了仿真实验，实验表明该方法能有效地将混沌背景中极其微弱的信号检测出来. 关键词： 混沌 神经网络 信号检测

The neural networks method for detecting weak signals under chaotic background

A method for detecting weak signals embebed in chaotic noise by neural networks based on the theory of phase space reconstruction of the complicated nonlinear system is presented. One-step predictive model for chaotic background is built by neural network that possess powerful cap ability of learning and nonlinear processing. Then the weak transient signal or periodic signal which is embedded in the chaotic background can be detected from the predictive error. And the detecting ability of this method when the chaotic background is mixed with white noiseis studied. The difference in the detecting principle for the transient signal and periodic signal is pointed out. The experiment which takes the Lorenz system as chaotic background shows this method can effectively detect very weak signals embedded in the chaotic background.