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Artificial Neural Nets and Genetic Algorithms pp 519–522Cite as

Comparison of Identification Techniques for Nonlinear Systems

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Abstract

The problem of identifying a dynamic process from experimentally derived data has received much attention in the technical literature. Classically, a least squares based method is used, having first established a suitable structure, to estimate the parameters of a linear discrete time model. Whilst recognising that such linear models are not suitable for all applications, they have gained widespread acceptance, since, in the main they are used with controller design algorithms which require models of that form.

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References

  1. Goodwin, G.C & Payne R. L., Dynamic System Identification: Experiment Analysis and Design. Academic Press, 1977.

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  6. Riva, A. et al, Dynamic System Identification by means of Neural Networks. Politecnico di Torino, Italy, 1993.

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  7. Hofland, A G. et al, Radial Basis Function Networks Applied to Process Control. University of Newcastle, UK, 1992.

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Authors and Affiliations

  1. Sunderland University, Chester Road, Sunderland, SR1 3SD, UK

    I. G. French, S. Ho & A. Adgar

Authors
  1. I. G. French
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  2. S. Ho
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  3. A. Adgar
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© 1995 Springer-Verlag/Wien

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French, I.G., Ho, S., Adgar, A. (1995). Comparison of Identification Techniques for Nonlinear Systems. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_134

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  • DOI: https://doi.org/10.1007/978-3-7091-7535-4_134

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-82692-8

  • Online ISBN: 978-3-7091-7535-4

  • eBook Packages: Springer Book Archive

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