Polynomial neural networks for signal processing in chaotic backgrounds

Summary form only given. Neural network signal processors can be trained to detect threshold signals in chaotic backgrounds where accurate global prediction, based upon learned behavior, may be possible. The neural network signal processing method is based upon the synthesis of a polynomial neural network (PNN) for global prediction of the chaotic background which is then subtracted in order to enhance the signal. As a demonstration of the PNN method, the authors have synthesized a PNN processor which produces a threshold signal-to-background improvement of over 20 dB in a chaotic background constructed from the Mackey-Glass delay differential equation.<
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