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Stephen A. Billings
Researcher at University of Sheffield
Publications - 502
Citations - 24198
Stephen A. Billings is an academic researcher from University of Sheffield. The author has contributed to research in topics: Nonlinear system & System identification. The author has an hindex of 70, co-authored 486 publications receiving 22892 citations. Previous affiliations of Stephen A. Billings include University of Manchester & Mount Sinai Hospital, Toronto.
Papers
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Orthogonal least squares methods and their application to non-linear system identification
TL;DR: Identification algorithms based on the well-known linear least squares methods of gaussian elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram- Schmidt, Householder transformation, Givens method, and singular value decomposition are reviewed.
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Input-output parametric models for non-linear systems Part II: stochastic non-linear systems
TL;DR: Recursive input-output models for non-linear multivariate discrete-time systems are derived, and sufficient conditions for their existence are defined.
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Non-linear system identification using neural networks
TL;DR: This paper investigates the identification of discrete-time nonlinear 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.
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Representations of non-linear systems: the NARMAX model
Sheng Chen,Stephen A. Billings +1 more
TL;DR: In this paper, it is shown that the NARMAX (Non-linear AutoRegressive Moving Average with eXogenous inputs) model is a general and natural representation of non-linear systems and contains, as special cases, several existing nonlinear models.
Book
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains
TL;DR: The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model as mentioned in this paper allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term Statistical and qualitative model validation methods that can be applied to any model class Generalised frequency response functions which provide significant insight into nonlinear behaviours.