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2017 | OriginalPaper | Buchkapitel

Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions

verfasst von : Mohd Najib Mohd Salleh, Noureen Talpur, Kashif Hussain

Erschienen in: Data Mining and Big Data

Verlag: Springer International Publishing

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Abstract

Adaptive neuro-fuzzy inference system (ANFIS) is efficient estimation model not only among neuro-fuzzy systems but also various other machine learning techniques. Despite acceptance among researchers, ANFIS suffers from limitations that halt applications in problems with large inputs; such as, curse of dimensionality and computational expense. Various approaches have been proposed in literature to overcome such shortcomings, however, there exists a considerable room of improvement. This paper reports approaches from literature that reduce computational complexity by architectural modifications as well as efficient training procedures. Moreover, as potential future directions, this paper also proposes conceptual solutions to the limitations highlighted.

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Nächstes Kapitel Acquisition of Knowledge in the Form of Fuzzy Rules for Cases Classification

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Titel: Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions
verfasst von: Mohd Najib Mohd Salleh
Noureen Talpur
Kashif Hussain
Verlag: Springer International Publishing
Buch: Data Mining and Big Data
Print ISBN: 978-3-319-61844-9

Electronic ISBN: 978-3-319-61845-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-61845-6_52

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