基于混沌和神经网络的弱信号探测

本文首先描述了混沌的定义，提出了一个判断时间序列是否具有混沌行为的实验准则，即时间序列要有有限维数的吸收子，一个为正数的Ｌｙａｐｕｎｏｖｅ指数（李氏指数）并且是局部可预测的，分析了神经网络的重构混沌时间序列相空间的性能和受白噪声干扰时重构相空间的能力，基于神经网络所具的强大学习和非线性处理能力和混沌的局部可预测性，提出了一种利用神经网络对淹没有混沌背景下的瞬态信号进行检测的方法，实验表明，这种方法

基于混沌和神经网络的弱信号探测

In this paper the definition of chaos is described at first, and then experimental rules are presented to declare whether a time series is chaotic. These rules are that time series should have an attractor with a finite dimensions, have a positive Lyapunove exponent at least, and be locally predicted. Neural network's abiltity to restruct phase space of chaotic time series and in the condition of populared by noise is also discussed. Based on the neural network's powerful ability of studying and nonlinear processing and local predictibility of chaos,a method to detect transient signal in the background of chaos is presented applying neural nerwork. The experimental results show this method can detect out a very weak target signal in the background of chaos.