Recurrent neural networks for identification of nonlinear systems
Xuemei Ren, Shumin Fei · 2002
A type of recurrent neural network is discussed which provides the potential for the modelling of unknown nonlinear systems with multi-inputs and multi-outputs. The proposed network is a generalization of the network described by Elman (1989). It is shown that the proposed network with appropriate neurons in the context layer can model unknown nonlinear systems. Based on a PID-like training objective function, the learning algorithm of the proposed network is considerably faster through the introduction of dynamic backpropagation, which is used to estimate the weights of both the feedforward and feedback connections. The techniques have been successfully applied to the modelling nonlinear plants and simulation results are included.