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Adaptive neuro fuzzy inference system

About: Adaptive neuro fuzzy inference system is a(n) research topic. Over the lifetime, 23244 publication(s) have been published within this topic receiving 428882 citation(s).

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Papers
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Journal ArticleDOI
Jyh-Shing Roger Jang1Institutions (1)
01 May 1993-
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.

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Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

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13,738 citations


Book
Li-Xin Wang1Institutions (1)
20 Aug 1996-

2,902 citations


Journal ArticleDOI

2,437 citations


Book
Li-Xin Wang1Institutions (1)
01 Feb 1994-
TL;DR: This paper presents a meta-analysis of the design and stability analysis of fuzzy identifiers of nonlinear dynamic systems fuzzy adaptive filters of adaptive fuzzy controllers using input-output linearization concepts.

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Abstract: Description and analysis of fuzzy logic systems training of fuzzy logic systems using back-propagation training of fuzzy logic systems using orthogonal least squares training of fuzzy logic systems using a table-lookup scheme training of fuzzy logic systems using nearest neighbourhood clustering comparison of adaptive fuzzy systems with artificial neural networks stable indirect adaptive fuzzy control of nonlinear systems stable direct adaptive fuzzy control of nonlinear systems design of adaptive fuzzy controllers using input-output linearization concepts design and stability analysis of fuzzy identifiers of nonlinear dynamic systems fuzzy adaptive filters.

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2,410 citations


Journal ArticleDOI
TL;DR: A general approach to quali- tative modeling based on fuzzy logic is discussed, which proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model.

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Abstract: This paper discusses a general approach to quali- tative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action.

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2,394 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202224
2021742
2020744
2019775
2018789
20171,048

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Topic's top 5 most impactful authors

Oscar Castillo

138 papers, 4.4K citations

Patricia Melin

124 papers, 3.8K citations

Shaocheng Tong

88 papers, 8.2K citations

Witold Pedrycz

75 papers, 1.6K citations

Ozgur Kisi

65 papers, 3.1K citations