Soft Computing

, Volume 21, Issue 9, pp 2357–2366 | Cite as

MSAFIS: an evolving fuzzy inference system

  • José de Jesús RubioEmail author
  • Abdelhamid Bouchachia
Methodologies and Application


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.


Intelligent systems Gradient descent Learning Big data 



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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Sección de Estudios de Posgrado e Investigación, ESIME AzcapotzalcoInstituto Politécnico NacionalMexico D.F.Mexico
  2. 2.Department of Computing and Informatics, Faculty of Science and TechnologyBournemouth UniversityDorsetUK

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