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Detection of static life characteristic signals based on fuzzy neural networks

JianJun Li, JianFeng Zhao

Life parameters signal has characteristics of extremely lowfrequency, low signal-to-noise ratio, and the easy submerged in strong clutter noises. Howto extract the characteristic parameters of life is a problem. This kind of problemcan be widely used in non-contact medical ward, and also puts forward a newdirection for weak signal detection. Themethod for detecting life signal based on fuzzy neural network, which is proposed via taking full advantage of processing fuzzy information of the fuzzy pattern recognition and self-learning of the neural network (NN) pattern recognition. Simulated results show that the method not only can completely descript life signals in the time-frequency domain, but improve the signal-to-noise ratio and the ability of detecting algorithm.Moreover, the method is effective and practical.