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Performance Evaluation of SIRMs Models for Classification Problems

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)

Abstract

The performance of Single-Input Rule-Modules (SIRMs) models are studied for classification proglems. In the original version of SIRMs models, each fuzzy if-then rule has a single real-valued output. The final output from an SIRMs model is discretized in application to classification problems. This paper proposes an extention to the SIRMs models where each fuzzy if-then rule has multiple realvalued outputs that represent the activation level of the corresponding classes. The classification performance of both the original and the extended models are evaluated through a series of computational experiments using two-dimensional pattern classification problems.

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References

  1. 1.
    Yubazaki, N.: Sirms dynamically connected fuzzy inference model and its applications. In: Proc. IFSA 1997, Prague, Czech, vol. 3, pp. 410–415 (1997), http://ci.nii.ac.jp/naid/10018385046/en/
  2. 2.
    Seki, H., Ishii, H., Mizumoto, M.: On the generalization of single input rule modules connected type fuzzy reasoning method. IEEE Transactions on Fuzzy Systems 16(5), 1180–1187 (2008), doi:10.1109/TFUZZ.2008.924182CrossRefGoogle Scholar
  3. 3.
    Seki, H., Ishii, H., Mizumoto, M.: On the monotonicity of fuzzy-inference methods related to t-s inference method. IEEE Transactions on Fuzzy Systems 18(3), 629–634 (2010), doi:10.1109/TFUZZ.2010.2046668CrossRefGoogle Scholar
  4. 4.
    Seki, H., Mizumoto, M.: On the equivalence conditions of fuzzy inference methods — part 1: Basic concept and definition. IEEE Transactions on Fuzzy Systems 19(6), 1097–1106 (2011), doi:10.1109/TFUZZ.2011.2160268CrossRefGoogle Scholar
  5. 5.
    Yi, J., Yubazaki, N., Hirota, K.: A proposal of sirms dynamically connected fuzzy inference model for plural input fuzzy control. Fuzzy Sets and Systems 125(1), 79–92 (2002), http://www.sciencedirect.com/science/article/pii/S0165011400001354, doi:10.1016/S0165-0114(00)00135-4MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Osaka Prefecture UniversitySakaiJapan
  2. 2.Osaka Institute of TechnologyOsaka CityJapan

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