Application of wavelets and neural networks to detect weak signal
Wei Zhang, Linlin Ge · 2008
This paper introduces the use of combined neural network model to guide model selection for detection of weak signal. It has been found that digital filters are not suitable for processing weak signals in noise, while wavelet neural network (WNN) is used to analyze weak digital signal and extract small-features. WNN is a time-frequency analysis adaptive system, which detects the subtle small changes in the signal spectrum. In this paper, we propose a new method is investigated by detecting the simulating weak signal in while noise. The results show that the WNN is a quite effective method for the extraction features of weak signal and improving the ratio of signal to noise.