Soft Computing

, Volume 21, Issue 9, pp 2357–2366

MSAFIS: an evolving fuzzy inference system

Methodologies and Application

DOI: 10.1007/s00500-015-1946-4

Cite this article as:
de Jesús Rubio, J. & Bouchachia, A. Soft Comput (2017) 21: 2357. doi:10.1007/s00500-015-1946-4
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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.

Keywords

Intelligent systems Gradient descent Learning Big data 

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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