MSAFIS: an evolving fuzzy inference system
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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.
KeywordsIntelligent 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.
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Conflict of interest
The authors declare that they have no conflict of interest.
- Bouchachia A (2008) Incremental Learning. Encyclopedia of Data Warehousing and Mining, pp 1006–1012Google Scholar
- Huang G-B, Saratchandran P, Sundararajan N (2004) An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans Syst Man Cybern Part B Cybern 34(6):2284–2292Google Scholar
- Iglesias JA, Tiemblo A, Ledezma A, Sanchis A (2015) Web news mining in an evolving framework. Inf Fusion. doi: 10.1016/j.inffus.2015.07.004
- Klancar G, Skrjanc I (2015) Evolving principal component clustering with a low run-timecomplexity for LRF data mapping. Appl Soft Comput 35:349–358Google Scholar
- Lughofer E, Cernuda C, Kindermann S, Pratama M (2015) Generalized smart evolving fuzzy systems. Evolv Syst. doi: 10.1007/s12530-015-9132-6
- Sayed-Mouchaweh M, Lughofer E (2015) Decentralized fault diagnosis approach without a global model for fault diagnosis of discrete event systems. Int J Control. doi: 10.1080/00207179.2015.1039594