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A Course in Fuzzy Systems and Control

About: The article was published on 1996-08-20 and is currently open access. It has received 2938 citations till now. The article focuses on the topics: Fuzzy set operations & Defuzzification.
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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

Book
14 Oct 2010
TL;DR: This paper presents a model for a Fuzzy Rule-Based System that automates the very labor-intensive and therefore time-heavy process of decision-making in the context of classical sets.
Abstract: Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.

994 citations

BookDOI
01 Jan 2004
TL;DR: This chapter discusses reinforcement learning in large, high-dimensional state spaces, model-based adaptive critic designs, and applications of approximate dynamic programming in power systems control.
Abstract: Foreword. 1. ADP: goals, opportunities and principles. Part I: Overview. 2. Reinforcement learning and its relationship to supervised learning. 3. Model-based adaptive critic designs. 4. Guidance in the use of adaptive critics for control. 5. Direct neural dynamic programming. 6. The linear programming approach to approximate dynamic programming. 7. Reinforcement learning in large, high-dimensional state spaces. 8. Hierarchical decision making. Part II: Technical advances. 9. Improved temporal difference methods with linear function approximation. 10. Approximate dynamic programming for high-dimensional resource allocation problems. 11. Hierarchical approaches to concurrency, multiagency, and partial observability. 12. Learning and optimization - from a system theoretic perspective. 13. Robust reinforcement learning using integral-quadratic constraints. 14. Supervised actor-critic reinforcement learning. 15. BPTT and DAC - a common framework for comparison. Part III: Applications. 16. Near-optimal control via reinforcement learning. 17. Multiobjective control problems by reinforcement learning. 18. Adaptive critic based neural network for control-constrained agile missile. 19. Applications of approximate dynamic programming in power systems control. 20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. 21. Helicopter flight control using direct neural dynamic programming. 22. Toward dynamic stochastic optimal power flow. 23. Control, optimization, security, and self-healing of benchmark power systems.

780 citations


Cites background from "A Course in Fuzzy Systems and Contr..."

  • ...Space limitations preclude surveying that literature here; a couple of accessible suggestions to the reader are [58], [62]....

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Journal ArticleDOI
TL;DR: All this is needed to implement a type-2 fuzzy logic system (FLS) is discussed, including join and meet under minimum/product t-norm, algebraic operations, properties of membership grades oftype-2 sets, andType-2 relations and their compositions.

700 citations


Cites background from "A Course in Fuzzy Systems and Contr..."

  • ...In [28], it is shown that this condition is equivalent to having the sup-star composition equal to 1....

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  • ...The validity of the sup-star composition for crisp sets is shown in [28]....

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Proceedings ArticleDOI
04 May 1998
TL;DR: A robust fuzzy logic system is introduced, one that can handle rule uncertainties and make use of type-2 fuzzy sets for this purpose, and a new operation that is called type-reduction is introduced.
Abstract: This paper introduces a robust fuzzy logic system, one that can handle rule uncertainties. We make use of type-2 fuzzy sets for this purpose. The development of a type-2 fuzzy logic system has led to a new operation that we call type-reduction. In the course of this development, we also study set operations on type-2 sets, properties of membership grades of type-2 sets, type-2 relations and their compositions, and defuzzification.

540 citations