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Fuzzy control and fuzzy systems

01 Jan 1989-
TL;DR: Presents extensive and updated material concerned with the methodology and algorithms of fuzzy sets considered mainly in the context of control engineering and system modeling and analysis.
Abstract: From the Publisher: Presents extensive and updated material concerned with the methodology and algorithms of fuzzy sets considered mainly in the context of control engineering and system modeling and analysis. Offers information on fuzzy sets and the concept of fuzzy control, reviewing selected applications and their origin. Discusses design aspects and theoretical developments in the design of fuzzy controllers. Includes comprehensive coverage of the paradigms and algorithms of fuzzy modeling.
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Journal ArticleDOI
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.
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. >

15,085 citations


Cites methods from "Fuzzy control and fuzzy systems"

  • ...This fuzzy modeling or fuzzy identification, first explored systematically by Takagi and Sugeno [54], has found numerous practical applications in control [48, 38], prediction and inference [18, 19]....

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Journal ArticleDOI
TL;DR: M Modes of information granulation (IG) in which the granules are crisp (c-granular) play important roles in a wide variety of methods, approaches and techniques, but this does not reflect the fact that in almost all of human reasoning and concept formation thegranules are fuzzy (f- Granular).

2,624 citations

Book
01 Jan 1993
TL;DR: Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic that can be found either as stand-alone control elements or as int ...
Abstract: Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

2,139 citations

Journal ArticleDOI
TL;DR: A survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models.
Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

1,575 citations


Additional excerpts

  • ...books and tutorial articles on the topic; see, for example, [7], [164], [165], [210], [236], [240], [241], [251], [259], [269], [299], [300], and [318]....

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  • ...management, finance, medicine, motor industry, robotics, and so on [7], [11], [14], [16], [65], [154], [164], [165], [210], [241], [248], [258], [261], [277], [289], [328], [334], [348]....

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Book
01 Aug 1996
TL;DR: A simple case in point is the problem of parking a car as discussed by the authors, where the final position of the car is not specified exactly, and if it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position.
Abstract: The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

1,483 citations