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MSAFIS: an evolving fuzzy inference system

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Abstract

In this paper, the problem of learning in big data is considered. To solve this problem, a new algorithm is proposed as the combination of two important evolving and stable intelligent algorithms: the sequential adaptive fuzzy inference system (SAFIS), and stable gradient descent algorithm (SGD). The modified sequential adaptive fuzzy inference system (MSAFIS) is the SAFIS with the difference that the SGD is used instead of the Kalman filter for the updating of parameters. The SGD improves the Kalman filter, because it first obtains a better learning in big data. The effectiveness of the introduced method is verified by two experiments.

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Acknowledgments

The authors are grateful to the editors and the reviewers for their valuable comments. The first author thanks the Secretaría de Investigación y Posgrado, Comisión de Operación y Fomento de Actividades Académicas, and Consejo Nacional de Ciencia y Tecnología for their help in this research.

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Authors and Affiliations

  1. Sección de Estudios de Posgrado e Investigación, ESIME Azcapotzalco, Instituto Politécnico Nacional, Av. de las Granjas no. 682, Col. Santa Catarina, 02250, Mexico D.F., Mexico

    José de Jesús Rubio

  2. Department of Computing and Informatics, Faculty of Science and Technology, Bournemouth University, Dorset, UK

    Abdelhamid Bouchachia

Authors
  1. José de Jesús Rubio
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  2. Abdelhamid Bouchachia
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Correspondence to José de Jesús Rubio.

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The authors declare that they have no conflict of interest.

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Communicated by V. Loia.

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de Jesús Rubio, J., Bouchachia, A. MSAFIS: an evolving fuzzy inference system. Soft Comput 21, 2357–2366 (2017). https://doi.org/10.1007/s00500-015-1946-4

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  • DOI: https://doi.org/10.1007/s00500-015-1946-4

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