System identification using a Fourier/Hopfield neural network

We use a Fourier/Hopfield network for the identification of a periodic system based on input-output data. The network automatically optimizes the model parameters and adapts by adding more neurons until the modeling error drops below a specified level. We show that, for periodic systems, Fourier basis functions are the best choice for system identification.