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Patchwork Neuro-fuzzy System with Hierarchical Domain Partition

  • Krzysztof Simiński
Part of the Advances in Intelligent and Soft Computing book series (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.

Keywords

Root Square Mean Error Fuzzy Inference System Isosceles Triangle Input Domain Good Generalisation Ability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Krzysztof Simiński
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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