Nonlinear system identification using diagonal recurrent neural networks

Abstract

The recurrent neural network is proposed for system identification of nonlinear dynamic systems. When the system identification is coupled with control problems, the real-time feature is very important, and a neuro-identifier must be designed so that it will converge and the training time will not be too long. The neural network should also be simple and implemented easily. A novel neuro-identifier, the diagonal recurrent neural network (DRNN), that fulfils these requirements is proposed. A generalized algorithm, dynamic backpropagation, is developed to train the DRNN. The DRNN was used to identify nonlinear systems, and simulation showed promising results.<>