Identification of nonlinear models with feed forward neural network and digital recurrent network

Abstract

Nonlinear system identification via Feedforward Neural Networks (FNN) and Digital Recurrent Network (DRN) is studied in this paper. The standard backpropagation algorithm is used to train the FNN. A dynamic backpropagation algorithm is employed to adapt weights and biases of the DRN. The neural networks are trained using the identified error between the model’s output and plant’s output. Results of simulations show that the application of the FNN and DRN to identification of complex nonlinear dynamics gives satisfactory results.