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International Conference on Parallel Processing and Applied Mathematics

PPAM 2003: Parallel Processing and Applied Mathematics pp 632–640Cite as

Neuro-Fuzzy versus Non-parametric Approach to System Modeling and Classification

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3019))

Abstract

This paper presents the comparative study concerning selected neuro-fuzzy systems and non-parametric methods. Moreover, a new idea of rough-neuro-fuzzy systems is suggested to solve the problem of missing features. The main applications of methods under study are system modeling and classification. The non-parametric methods are based on density and regression estimates. They converge to the optimal solution when the sample size grows large. The neuro-fuzzy structures do not possess convergence properties however they are applied successfully in modeling and classification problems. The methods are illustrated on several simulation examples.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Al. Armii Krajowej 36, 42-200, Czestochowa, Poland

    Robert Nowicki

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  1. Robert Nowicki
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Editor information

Editors and Affiliations

  1. Institute of Computational and Information Sciences, Czestochowa University of Technology,  

    Roman Wyrzykowski

  2. Computer Science Department, University of Tennessee, TN 37996-3450, Knoxville, USA

    Jack Dongarra

  3. Systems Research Institute, Polish Academy of Science, Warsaw, Poland

    Marcin Paprzycki

  4. Informatics & Mathematical Modeling, Technical University of Denmark, DK-2800, Lyngby, Denmark

    Jerzy Waśniewski

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© 2004 Springer-Verlag Berlin Heidelberg

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Nowicki, R. (2004). Neuro-Fuzzy versus Non-parametric Approach to System Modeling and Classification. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2003. Lecture Notes in Computer Science, vol 3019. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24669-5_83

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  • DOI: https://doi.org/10.1007/978-3-540-24669-5_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21946-0

  • Online ISBN: 978-3-540-24669-5

  • eBook Packages: Springer Book Archive

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