Many real-world systems exhibit complex nonlinear characteristics and cannot be treated satisfactorily using linear systems theory. A neural network which has the ability to learn sophisticated nonlinear relationships provides an ideal means of modelling complicated nonlinear systems. This paper addresses the issues related to the identification of nonlinear discrete-time dynamic systems using neural networks. Three network architectures, namely the multi-layer perceptron, the radial basis function network and the functional-link network, are presented and several learning or identification algorithms are derived. Advantages and disadvantages of these structures are discussed and illustrated using simulated and real data. Particular attention is given to the connections between existing techniques for nonlinear systems identification and some aspects of neural network methodology, and this demonstrates that certain techniques employed in the neural network context have long been developed by the control e...

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Neural Networks for Nonlinear Dynamic System Modelling and Identification

Recursive identification of non-linear systems is investigated using radial basis function networks. A novel approach is adopted which employs a hybrid clustering and least squares algorithm. The recursive clustering algorithm adjusts the centres of the radial basis function network while the recursive least squares algorithm estimates the connection weights of the network. Because these two recursive learning rules are both linear, rapid convergence is guaranteed and this hybrid algorithm significantly enhances the real-time or adaptive capability of radial basis function models. The application to simulated real data are included to demonstrate the effectiveness of this hybrid approach.

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Recursive hybrid algorithm for non-linear system identification using radial basis function networks

Properties of neural network performance are investigated by studying the modelling of non-linear dynamical systems Network complexity, node selection, prediction and the effects of noise are studied and some new metrics of performance are introduced The results are illustrated with both simulated and industrial examples

/pdf/properties-of-neural-networks-with-applications-to-modelling-esf1hypsme.pdf

Properties of neural networks with applications to modelling non-linear dynamical systems

Soft analyzers for a sulfur recovery unit

A comparison of the backpropagation and recursive prediction error algorithms for training neural networks

Multi-layered neural networks offer an exciting alternative for modelling complex non-linear systems This paper investigates the identification of discrete-time non-linear systems using neural networks with a single hidden layer New parameter estimation algorithms are derived for the neural network model based on a prediction error formulation and the application to both simulated and real data is included to demonstrate the effectiveness of the neural network approach

S. Chen

Papers

Non-Linear Systems Identification Using Neural Networks