MSAFIS: an evolving fuzzy inference system.

de Jesús Rubio, J. and Bouchachia, A., 2017. MSAFIS: an evolving fuzzy inference system. Soft Computing, 21 (9), 2357 - 2366.

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DOI: 10.1007/s00500-015-1946-4

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.

Item Type:Article
ISSN:1432-7643
Uncontrolled Keywords:Intelligent systems; Gradient descent; Learning; Big data
Group:Faculty of Science & Technology
ID Code:29408
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:27 Jun 2017 07:54
Last Modified:27 Jun 2017 07:54

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