Real time identification and control of dynamic systems using recurrent neural networks

Recently, several recurrent neural networks for solving constraint optimization problems were developed. In this paper, we propose a novel approach to the use of a projection neural network for solving real time identification and control of time varying systems. In addition to low complexity and simple structure, the proposed neural network can solve wider classes of time varying systems compare with other neural networks that are used for optimization such as Hopfield neural networks. Simulation results demonstrate the effectiveness and characteristics of the proposed neural network compared with a Hopfield neural network.