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2017 | OriginalPaper | Chapter

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

Authors : Mohd Najib Mohd Salleh, Noureen Talpur, Kashif Hussain

Published in: Data Mining and Big Data

Publisher: 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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previous chapter Improved Stability and Stabilization Criteria for T-S Fuzzy Systems with Distributed Time-Delay
next chapter Acquisition of Knowledge in the Form of Fuzzy Rules for Cases Classification
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Metadata
Title
Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions
Authors
Mohd Najib Mohd Salleh
Noureen Talpur
Kashif Hussain
Copyright Year
2017
Book
Data Mining and Big Data

Print ISBN: 978-3-319-61844-9

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

Copyright Year: 2017

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

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