Modelling of nonlinear systems from input-output data for state space realization

In this paper, we examine data driven modelling procedures for creating a discrete-time input-output map that can be transformed into an observable state space form. We first present previous results of a model form that guarantees the existence of an observable state space realization, as well as the state equations that can be implemented using that form. We then examine the feasibility of NARMA models, feedforward neural networks, and nodal link perceptron networks with local basis functions in creating the model. Simulation results are shown for these model types, as well as a linear model for comparison