Fuzzy classification in dynamic environments

This source preferred by Hamid Bouchachia

This data was imported from DBLP:

Authors: Bouchachia, A.

Journal: Soft Comput.

Volume: 15

Pages: 1009-1022

DOI: 10.1007/s00500-010-0657-0

This data was imported from Scopus:

Authors: Bouchachia, A.

Journal: Soft Computing

Volume: 15

Issue: 5

Pages: 1009-1022

eISSN: 1433-7479

ISSN: 1432-7643

DOI: 10.1007/s00500-010-0657-0

The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min-max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches. © 2010 Springer-Verlag.

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