Neural networks for system identification

Two approaches are presented for utilization of neural networks in identification of dynamical systems. In the first approach, a Hopfield network is used to implement a least-squares estimation for time-varying and time-invariant systems. The second approach, which is in the frequency domain, utilizes a set of orthogonal basis functions and Fourier analysis to construct a dynamic system in terms of its Fourier coefficients. Mathematical formulations are presented, along with simulation results.>