【摘要】 数字示波器不能测量混沌背景中的微弱信号,该文结合混沌和神经网络构建检测模型实现该功能。运用混沌时间序列的相空间重构理论计算嵌入维数作为神经网络的输入维来构建网络模型,并采用单步预测方法,在混沌状态下直接测量混沌背景中微弱信号,获取微弱信号的波形。该方法能够测量微弱信号的时域参数,测量范围宽,逼近目标精度高,计算量小。实验结果证明了该方法具有很强的实用性。更多还原

【Abstract】 Digital oscilloscope can not measure week singal in chaotic background. A method using Elman neural network is described to achieve signal parameter detection in chaotic background. With the phase space reconstruction theory on time series, the embedded dimension is calculated and used as the in-put dimension of a neural network considered. By adopting the single-step prediction method, the weak signals are detected directly and their waveforms can be gained as well in the chaotic state. Result shows that the method studied in this paper is superior to the existing detection principles. Its feasibility and practicability have been proved by the experiments.更多还原