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Non-singleton Interval Type-2 Fuzzy Systems as Integration Methods in Modular Neural Networks Used Genetic Algorithms to Design

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Nature-Inspired Design of Hybrid Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 667))

Abstract

In this paper, we propose the use of Non-Singleton Interval Type-2 Fuzzy Systems (NSIT2FI) automatically designed through genetic algorithms as integration method of modular neural networks (MNN’s) for multimodal biometrics. The goal is to obtain such fuzzy systems as integrators, better recognition rate, and best mean square error in MNN. The results shown comparison between interval type-2 fuzzy systems and Non-singleton Type-2 Fuzzy Systems, where we can observe showing a significant difference that we can get higher recognition rate using non-singleton type-2 fuzzy logic.

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References

  1. J.-S.R. Jang, C.-T. Sun, E. Mizutani, Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.

    Google Scholar 

  2. J. Mendel, UNCERTAIN Rule-Based Fuzzy Logic Systems, Introduction and New Directions, Prentice Hall, 2001.

    Google Scholar 

  3. J. Mendel, Uncertain Rule-based Fuzzy Logic Systems, Prentice Hall, 2001.

    Google Scholar 

  4. Mendel, J., John, R., «Type-2 fuzzy sets made simple,» IEEE Transactions on Fuzzy Systems , nº 10, pp. 117-127, 2002.

    Google Scholar 

  5. Sahab, Nazanin and Hagras, Hani, «Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications.» International Journal of Computers, Communications and Control , vol. 3, nº 5, pp. 503-529, 2011.

    Google Scholar 

  6. T. S. M. Takagi, «Fuzzy identification of systems and its application to modeling and control,» IEEE Transactions on Systems, Man, and Cybernetics, vol. 1, nº 15, 1985.

    Google Scholar 

  7. Qilian Liang, Jerry M. Mendel, «Interval Type-2 Fuzzy Logic Systems:,» IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 8, nº 5, pp. 535-550, 2000.

    Google Scholar 

  8. Liang, Q., Mendel, J. , «Interval type-2 fuzzy logic systems: theory and design,» IEEE Transactions on Fuzzy Systems, vol. 5, nº 8, pp. 535-550, 2000.

    Google Scholar 

  9. Mendel, J., Mouzouris, George C., «Type-2 fuzzy logic systems,» IEEE Transactions on Fuzzy Systems, nº 7, pp. 643-658, 1999.

    Google Scholar 

  10. O. Castillo, P. Melin, Studies in Fuzziness and Soft Computing, Type-2 Fuzzy Logic: Theory and Applications, Tijuana , Baja California: Springer-Verlag Berlin Heidelberg, 2008.

    Google Scholar 

  11. Ricardo Martínez-Soto, Oscar Castillo, Juan R. Castro, «Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers. : 3-18,» de Recent Advances on Hybrid Approaches for Designing Intelligent Systems, 2014, pp. 3-18.

    Google Scholar 

  12. Denisse Hidalgo, Patricia Melin, Oscar Castillo, «Type-1 and Type-2 Fuzzy Inference Systems as Integration Methods in Modular Neural Networks for Multimodal Biometry and its Optimization with Genetic Algorithms,» Journal of Automation, Mobil Robotics & Intelligent Systems, vol. 2, nº 1, pp. 53-73, 2008.

    Google Scholar 

  13. Juan R. Castro, Oscar Castillo, Luis G. Martínez, «Interval Type-2 Fuzzy Logic Toolbox.,» Engineering Letters, vol. 1, nº 15, pp. 89-98, 2007.

    Google Scholar 

  14. Karnik, N.N., Mendel, J., «Centroid of a type-2 fuzzy set,» Information Sciences, Vols. %1 de %2(1-4), nº 132, pp. 195-220, 2001.

    Google Scholar 

  15. Denisse Hidalgo, Oscar Castillo, Patricia Melin, «Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms,» Information Sciences, vol. 179, nº Issue 13, p. 2123–2145, 13 June 2009.

    Google Scholar 

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Authors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Denisse Hidalgo & Patricia Melin

  2. School of Engineering, UABC University, Tijuana, Mexico

    Juan R. Castro

Authors
  1. Denisse Hidalgo
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  2. Patricia Melin
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  3. Juan R. Castro
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Corresponding author

Correspondence to Denisse Hidalgo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, BC, Mexico

    Oscar Castillo

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Hidalgo, D., Melin, P., Castro, J.R. (2017). Non-singleton Interval Type-2 Fuzzy Systems as Integration Methods in Modular Neural Networks Used Genetic Algorithms to Design. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_56

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  • DOI: https://doi.org/10.1007/978-3-319-47054-2_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47053-5

  • Online ISBN: 978-3-319-47054-2

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