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S.A. Billings

Bio: S.A. Billings is an academic researcher. The author has contributed to research in topics: Deep learning & Probabilistic neural network. The author has an hindex of 1, co-authored 1 publications receiving 42 citations.

Papers
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01 Apr 1989
TL;DR: This paper investigates the identification of discrete-time non-linear systems using neural networks with a single hidden layer using new parameter estimation algorithms derived for the neural network model based on a prediction error formulation.
Abstract: 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

42 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper addresses the issues related to the identification of nonlinear discrete-time dynamic systems using neural networks with particular attention to the connections between existing techniques for nonlinear systems identification and some aspects of neural network methodology.
Abstract: 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...

618 citations

Journal ArticleDOI
TL;DR: A novel approach is adopted which employs a hybrid clustering and least squares algorithm which significantly enhances the real-time or adaptive capability of radial basis function models.
Abstract: 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.

359 citations

Journal ArticleDOI
TL;DR: Properties of neural network performance are investigated by studying the modelling of non-linear dynamical systems, including node selection, prediction, prediction and the effects of noise.
Abstract: 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

266 citations

Journal ArticleDOI
TL;DR: This work deals with the design and implementation of soft sensors for a Sulfur Recovery Unit (SRU) in a refinery, where the measurements considered in this work are very important for the environmental impact of the refinery, as they regard pollutant acid gas emissions.

89 citations

Journal ArticleDOI
TL;DR: Results based on simulated systems, the prediction of Canadian lynx data, and the modelling of an automotive diesel engine indicate that the recursive prediction error algorithm is far superior to backpropagation.

69 citations