Discrete-time domain poles zeros identification using back propagation neural networks

Describes a back propagation neural network applying to poles zeros identification in discrete time domain. Traditional recursive least squares (RLS) algorithm is time consuming and sensitive to noise. Neural networks possess massive parallel processing capability and noise immunity, the time and noise constraints can be eliminated. The results are encouraging and demonstrate that neural networks offer new promising directions towards solving system identification problems radically