Nonlinear system identification and adaptive control using polynomial networks

This work introduces a new nonlinear computational model called polynomial network for identification and adaptive control of nonlinear dynamical systems. The network approximates the Volterra systems or recursive polynomial systems. The approximation properties of this network are compared with the sigmoid networks. The results show that the polynomial network constructs a simpler and smaller model and requires less training data. Also, the model realized by the polynomial network is mathematically tractable. The feasibility of using this model for direct model reference adaptive control of a class of nonlinear systems is demonstrated.