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WIREs Data Mining Knowl Discov
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Evolving fuzzy systems for data streams: a survey

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Abstract Evolving fuzzy systems (EFSs) can be regarded as intelligent systems based on fuzzy rule‐based or neuro‐fuzzy models with the ability to learn continuously and to gradually develop with the objective of enhancing their performance. Such systems learn in online mode by analyzing incoming samples, and adjusting both structure and parameters. The objective of this chapter is to present a brief overview of some early as well as recent EFSs by focusing on their architecture, design algorithms along with the merits and demerits, and various applications. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 461–476 DOI: 10.1002/widm.42 This article is categorized under: Technologies > Computational Intelligence

Graph indicating growth of evolving systems research in the past decade. (Based on the data retrieved from ISI Web of Science with key words: [evolving OR adaptive OR self‐developing OR self‐learning OR self‐organizing) AND (fuzzy rules OR fuzzy neural network OR neuro‐fuzzy)].

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A general architecture of evolving neuro‐fuzzy system (Takagi–Sugeno‐type rules).

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Evolving Takagi–Sugeno clustering discounting distant sample p, which may not be actually an outlier.

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A general architecture of evolving fuzzy rule‐based system.

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