Abstract
In this paper, we propose a hybrid neural network (HNN) for identification of unknown continuous time nonlinear dynamic systems. HNN can identify dynamics of unknown systems by using only inputs and outputs. It is the salient feature of our proposal that HNN does not need states of the system nor differentials of outputs. The existence of HNN which approximates input-output relationship of unknown dynamic system within any precision is shown. In addition to HNN, two multilayered neural networks (MNNs) are used as a state observer for HNN and a state feedback controller. Integrating HNN and these two MNNs, a controller for the unknown nonlinear plant is constructed. Simulation results show the validity of our proposal.
