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Realization of XOR by SIRMs Connected Fuzzy Inference Method

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Next-Generation Applied Intelligence (IEA/AIE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5579))

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Abstract

The single input rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional type single input rule modules connected fuzzy inference method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs method can not realize XOR (Exclusive OR). In this paper, we propose a “neural network-type SIRMs method” which unites the neural network and SIRMs method, and show that this method can realize XOR. Further, a learning algorithm of the proposed SIRMs method is derived by steepest descent method, and is shown to be superior to the conventional SIRMs method and neural network by applying to identification of nonlinear functions.

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References

  1. Yubazaki, N., Yi, J., Otani, M., Hirota, K.: SIRMs dynamically connected fuzzy inference model and its applications. In: Proc. IFSA 1997, vol. 3, pp. 410–415 (1997)

    Google Scholar 

  2. Yi, J., Yubazaki, N., Hirota, K.: A proposal of SIRMs dynamically connected fuzzy inference model for plural input fuzzy control. Fuzzy Sets Syst. 125, 79–92 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Yi, J., Yubazaki, N., Hirota, K.: A new fuzzy controller for stabilization of parallel-type double inverted pendulum system. Fuzzy Sets Syst. 126, 105–119 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Seki, H., Ishii, H., Mizumoto, M.: On the generalization of single input rule modules connected type fuzzy reasoning method. IEEE Trans. Fuzzy Syst. 16, 1180–1187 (2008)

    Article  Google Scholar 

  5. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15, 116–132 (1985)

    Article  MATH  Google Scholar 

  6. Jang, J.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst., Man Cybern. 23, 665–685 (1993)

    Article  Google Scholar 

  7. Wang, J.S., George, C.S.: Self-adaptive neuro-fuzzy inference systems for classification applications. IEEE Trans. Fuzzy Syst. 10, 790–802 (2002)

    Article  Google Scholar 

  8. Iyatomi, H., Hagiwara, M.: Adaptive fuzzy inference neural network. Pattern Recognition 37, 2049–2057 (2004)

    Article  Google Scholar 

  9. Juang, C.F.: A TSK type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Trans. Fuzzy Syst. 10, 155–170 (2002)

    Article  Google Scholar 

  10. Yu, W., Li, X.: Fuzzy identification using fuzzy neural networks with stable learning algorithms. IEEE Trans. Fuzzy Syst. 12, 411–420 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. Osaka University, Suita, Osaka, Japan

    Hirosato Seki, Satoshi Watanabe & Hiroaki Ishii

  2. Japan Society for the Promotion of Science (JSPS), Japan

    Hirosato Seki

  3. Osaka Electro-Communication University, Neyagawa, Osaka, Japan

    Masaharu Mizumoto

Authors
  1. Hirosato Seki
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  2. Satoshi Watanabe
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  3. Hiroaki Ishii
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  4. Masaharu Mizumoto
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 700, Tainan, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

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© 2009 Springer-Verlag Berlin Heidelberg

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Seki, H., Watanabe, S., Ishii, H., Mizumoto, M. (2009). Realization of XOR by SIRMs Connected Fuzzy Inference Method. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_26

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  • DOI: https://doi.org/10.1007/978-3-642-02568-6_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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