Experiment design considerations for non-linear system identification using neural networks

Although the non-linear modelling capability of neural networks is widely accepted there remain many issues to be addressed relating to the design of a successful identification experiment. In particular, the choices of process excitation signal, data sample time and neural network model structure all contribute to the success, or failure, of a neural network's ability to reliably approximate the dynamic behaviour of a process. This paper examines the effects of these design considerations in an application of a multi-layered perceptron neural network to identifying the non-linear dynamics of a simulated pH process. The importance of identification experiment design for obtaining a network capable of both accurate single step and long range predictions is illustrated. The use of model parsimony indices, model validation tests and histogram analysis of training data for design of a neural network identification experiment are investigated.