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Finite Convergence of MRI Neural Network for Linearly Separable Training Patterns

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

MRI (Madaline Rule I) neural network has wide applications. A finite convergence for the training of MRI neural network is proved for linearly separable training patterns.

Partly supported by the National Natural Science Found of China, the Basic Research Program of the Committee of Science, Technology and Industry of National Defense of China.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Dalian University of Technology, Dalian, 116024, China

    Lijun Liu & Wei Wu

Authors
  1. Lijun Liu
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  2. Wei Wu
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Liu, L., Wu, W. (2004). Finite Convergence of MRI Neural Network for Linearly Separable Training Patterns. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_48

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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