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A novel neurocomputing approach to nonlinear stochastic state estimation

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Computational Intelligence Theory and Applications (Fuzzy Days 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1226))

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Abstract

This paper presents a novel neuro-computing approach to the problem of state estimation by means of an hybrid combination of Hopfield neural network whose capability of solving certain optimization problems is well-known and feedforward multilayer neural net which is very popular because of its universal approximation property. This neuro-estimator is very appropriate for the real-time implementation of linear or/and especially nonlinear state estimators. Simulation results shows the effectiveness of the proposed method.

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References

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Authors and Affiliations

  1. Electical Engineering Departmant, Amirkabir University of Technology, 15914, Tehran, Iran

    M. B. Menhaj & F. Rajaii Salmasi

Authors
  1. M. B. Menhaj
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  2. F. Rajaii Salmasi
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Editor information

Bernd Reusch

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© 1997 Springer-Verlag Berlin Heidelberg

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Menhaj, M.B., Salmasi, F.R. (1997). A novel neurocomputing approach to nonlinear stochastic state estimation. In: Reusch, B. (eds) Computational Intelligence Theory and Applications. Fuzzy Days 1997. Lecture Notes in Computer Science, vol 1226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62868-1_97

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  • DOI: https://doi.org/10.1007/3-540-62868-1_97

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62868-2

  • Online ISBN: 978-3-540-69031-3

  • eBook Packages: Springer Book Archive

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