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Systems identification with GMDH neural networks: a multi-dimensional case

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Artificial Neural Nets and Genetic Algorithms

Abstract

This paper presents a relatively new identification method based on Artificial Neural Networks, which can be used for multi-input multi-output systems. In particular, a Group Method of Data Handling neural network with dynamic neurons is considered. The final part of this work contains an illustrative example regarding the application of the proposed approach to a fault detection system.

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References

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© 2003 Springer-Verlag Wien

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Mrugalski, M., Arinton, E., Korbicz, J. (2003). Systems identification with GMDH neural networks: a multi-dimensional case. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_22

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  • DOI: https://doi.org/10.1007/978-3-7091-0646-4_22

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-00743-3

  • Online ISBN: 978-3-7091-0646-4

  • eBook Packages: Springer Book Archive

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