The Study of the Robust Learning Algorithm for Neural Networks (original) (raw)

Abstract

In this paper, we propose the robust learning algorithm for neural networks. The suggested algorithm is obtaining the expanded Kalman filter in the Krein space. We show that this algorithm can be applied to identify the nonlinear system in the presence of the observed noise and system noise.

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References

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Authors and Affiliations

1. Department of Electric and Electronic Engineering, School of Science and Engineering, Kinki University, Osaka, 577-8502, Japan
   Shigenobu Yamawaki

Authors

1. Shigenobu Yamawaki

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Editors and Affiliations

1. School of Design, Engineering and Computing, Bournemouth University, UK
   Bogdan Gabrys
2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK
   Robert J. Howlett
3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, SA, 5095, Mawson Lakes, Australia
   Lakhmi C. Jain

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© 2006 Springer-Verlag Berlin Heidelberg

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Yamawaki, S. (2006). The Study of the Robust Learning Algorithm for Neural Networks. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004\_110

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• DOI: https://doi.org/10.1007/11893004\_110
• Publisher Name: Springer, Berlin, Heidelberg
• Print ISBN: 978-3-540-46537-9
• Online ISBN: 978-3-540-46539-3
• eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science

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