Topic
Fuzzy control system
About: Fuzzy control system is a research topic. Over the lifetime, 50826 publications have been published within this topic receiving 862721 citations.
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TL;DR: Today, the authors have microwave ovens and washing machines that can figure out on their own what settings to use to perform their tasks optimally; cameras that come close to professional photographers in picture-taking ability; and many other products that manifest an impressive capability to reason, make intelligent decisions, and learn from experience.
Abstract: Prof. Zadeh presented a comprehensive lecture on fuzzy logic, neural networks, and soft computing. In addition, he lead a spirited discussion of how these relatively new techniques may be applied to safety evaluation of time variant and nonlinear structures based on identification approaches. The abstract of his lecture is given as follows.
1,390 citations
Book•
01 Jan 1997
TL;DR: Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy Systems.
Abstract: From the Publisher:
Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples of specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.
1,349 citations
TL;DR: In this paper, fuzzy logic is viewed in a nonstandard perspective and the cornerstones of fuzzy logic-and its principal distinguishing features-are: graduation, granulation, precisiation and the concept of a generalized constraint.
1,253 citations
TL;DR: It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.
Abstract: This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.
1,239 citations
Book•
01 Mar 1998
TL;DR: This is an interdisciplinary book on neural networks, statistics and fuzzy systems that establishes a general framework for adaptive data modeling within which various methods from statistics, neural networks and fuzzy logic are presented.
Abstract: From the Publisher:
This is an interdisciplinary book on neural networks, statistics and fuzzy systems. A unique feature is the establishment of a general framework for adaptive data modeling within which various methods from statistics, neural networks and fuzzy logic are presented. Chapter summaries, examples and case studies are also included.[Includes companion Web site with ... Software for use with the book.
1,232 citations