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Self-spawning neuro-fuzzy system for rule extraction

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Abstract

In this paper we propose self-spawning neuro-fuzzy system (SSNFS), a new neuro-fuzzy system to derive fuzzy rules from data. The SSNFS model is based on a generic definition of incremental perceptron and a new learning algorithm that is capable of both structural (rule) learning and parametric learning. It constructs the fuzzy system by detecting a suitable number of rule patches and their positions and shapes in the input space. Initially the rule base consists of one single fuzzy rule; during the iterative learning process the rule base expands according to a supervised spawning validity measure. The rule induction process terminates when a given stop criterion is satisfied. SSNFS is very general since it does not require the prior knowledge about the input space and can be used in any application based on the scatter-partitioning fuzzy system. To demonstrate the effectiveness and applicability of our algorithm, we present a synthetic example and real-world modelling problems.

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Notes

  1. The Iris data was obtained from http://www.ics.uci.edu/mlearn/MLRepository.html via a free, public ftp site.

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Acknowledgments

This research has been supported by a grant from Hong Kong RGC CERG 9041020 (CityU 118205).

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Authors and Affiliations

  1. School of Creative Media, City University of Hong Kong, Hong Kong, Hong Kong SAR

    Zhi-Qiang Liu, Tao Guan & Ya-Jun Zhang

  2. Department of Computer Science and Software Engineering, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Ya-Jun Zhang

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  1. Zhi-Qiang Liu
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  2. Tao Guan
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  3. Ya-Jun Zhang
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Correspondence to Zhi-Qiang Liu.

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Liu, ZQ., Guan, T. & Zhang, YJ. Self-spawning neuro-fuzzy system for rule extraction. Soft Comput 13, 1013–1025 (2009). https://doi.org/10.1007/s00500-008-0375-z

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