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Patchwork Neuro-fuzzy System with Hierarchical Domain Partition

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Computer Recognition Systems 3

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 57))

Summary

The paper presents the patchwork hierarchical domain partition in the neuro-fuzzy system with parameterized consequences. The hierarchical domain partition has the advantages of grid partition and clustering. It avoids the curse of dimensionality and reduces the occurrence of areas with low membership to all regions. The paper depicts the iterative hybrid procedure of hierarchical split. The splitting procedure estimates the best way of creating of the new region: (1) based on finding and splitting the region with the highest contribution to the error of the system or (2) creation of patch region for the highest error area. The paper presents the results of experiments on real life and synthetic datasets. This approach can produce neuro-fuzzy inference systems with better generalisation ability and subsequently lower error rate.

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Author information

Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Krzysztof Simiński

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  1. Krzysztof Simiński
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Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, WybrzezeWyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzynski  & Michal Wozniak  & 

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Simiński, K. (2009). Patchwork Neuro-fuzzy System with Hierarchical Domain Partition. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_2

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  • DOI: https://doi.org/10.1007/978-3-540-93905-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-93904-7

  • Online ISBN: 978-3-540-93905-4

  • eBook Packages: EngineeringEngineering (R0)

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