Nonlinear system process prediction using neural networks

A novel iterative technique is proposed by the authors in order to construct a discrete-time nonlinear dynamical system predictor from experimental input-output pairs. The proposed technique reduces the memory amount required to construct the predictor taking into account the intrinsic redundancy of the input-output pairs of the experimental data due to underlying physical laws. The paper deals with SISO systems, anyway it is easy to extend the results to the MIMO case. The technique is very well suited to work in conjunction with the cerebellar model arithmetic computer (CMAC) or radial basis functions (RBF). A convergence discussion on the proposed algorithm is provided. Finally computer simulations verify the stated theory