Recurrent multilayer perceptron for nonlinear system identification

A hybrid feedforward/feedback neural network, namely a recurrent multilayer perceptron, is used to identify nonlinear dynamic systems in an input/output sense. The feedforward portion of the network architecture provides the well-known curve-fitting character, while the local information feedback, through recurrency and crosstalk, permits the capture of the temporal aspects of the unknown system. A dynamic learning algorithm is used to train the proposed network in a supervised manner. The derived dynamic learning algorithm exhibits a computationally desirable characteristic: both network sweeps involved in the algorithm are performed forward, enhancing its parallel implementation. The capability of the recurrent multilayer perceptron network to identify nonlinear systems, using dynamic backpropagation learning, is demonstrated through a simple example. The simulation results are encouraging, though test of the identification method on a real-world system is still under investigation