Results 201 to 210 of about 79,086 (255)
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1999
Tutorials - an overview of fuzzy modelling and model-based fuzzy control, R. Babuska fuzzy control - from heuristic PID to optimization-based nonlinear control, K.-E. Arzen and M. Johansson surveys on advanced methodologies - neuro-fuzzy methods, D. Nauck complexity reduction methods for fuzzy systems design, M.
Henk B Verbruggen, Robert Babuška
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Tutorials - an overview of fuzzy modelling and model-based fuzzy control, R. Babuska fuzzy control - from heuristic PID to optimization-based nonlinear control, K.-E. Arzen and M. Johansson surveys on advanced methodologies - neuro-fuzzy methods, D. Nauck complexity reduction methods for fuzzy systems design, M.
Henk B Verbruggen, Robert Babuška
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Fuzzy logic in control systems: fuzzy logic controller. I
IEEE Transactions on Systems, Man, and Cybernetics, 1990For pt.I see ibid., vol.20, no.2, p.404-18, 1990. The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined. Several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated ...
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Microelectronics Journal, 1995
Fuzzy logic is increasingly being incorporated in systems to provide robust and effective control in a wide range of applications. The method is based on a many-valued logic which enables general principles and expert knowledge to be used to provide control rules and procedures.
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Fuzzy logic is increasingly being incorporated in systems to provide robust and effective control in a wide range of applications. The method is based on a many-valued logic which enables general principles and expert knowledge to be used to provide control rules and procedures.
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Fuzzy Logic Programming and Fuzzy Control
Studia Logica, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Journal of Biotechnology, 1992
This paper describes a fuzzy sets method which is very useful for handling uncertainties and essential for knowledge acquisition of a human expert. Kinetics of a reactor is often complex and not trivial to describe by mathematical equations. Reactor control by traditional control technology is therefore difficult. A novel technology is presented.
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This paper describes a fuzzy sets method which is very useful for handling uncertainties and essential for knowledge acquisition of a human expert. Kinetics of a reactor is often complex and not trivial to describe by mathematical equations. Reactor control by traditional control technology is therefore difficult. A novel technology is presented.
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Alternative fuzzy controller logics
Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference, 2002We compare six fuzzy logics used to control a simulated inverted pendulum "plant". Among the six is the standard "Mamdani" fuzzy logic. All systems contain eleven rules and are optimized for fuel economy. Mamdani's logic fares very well in the comparisons, but serious challengers are identified. >
B. Schott, T. Whalen
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2000
The fuzzy set notion enables us to define the progressive membership of an element to a class, that is to say it belongs to this class more or less strongly. The membership of an object in a class defines a degree of membership given between 0 and 1.
Mohand Mokhtari, Michel Marie
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The fuzzy set notion enables us to define the progressive membership of an element to a class, that is to say it belongs to this class more or less strongly. The membership of an object in a class defines a degree of membership given between 0 and 1.
Mohand Mokhtari, Michel Marie
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[1992 Proceedings] IEEE International Conference on Fuzzy Systems, 1992
A systematic design procedure for fuzzy linguistic controllers with adaptive or learning capability is introduced. The design is based on stability and hierarchy of identification and control. The fuzzy rule-base is stored in a fuzzy hypercube and the fuzzy control action is computed via a fuzzy inference mechanism.
H. Kang, G. Vachtsevanos
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A systematic design procedure for fuzzy linguistic controllers with adaptive or learning capability is introduced. The design is based on stability and hierarchy of identification and control. The fuzzy rule-base is stored in a fuzzy hypercube and the fuzzy control action is computed via a fuzzy inference mechanism.
H. Kang, G. Vachtsevanos
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1992
Fuzzy Set Theory, introduced by Zadeh in 1965 [77], has been the subject of much controversy and debate. In recent years, it has found many applications in a variety of fields. Among the most successful applications of this theory has been the area of Fuzzy Logic Control (FLC) initiated by the work of Mamdani and Assilian [36]. FLC has had considerable
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Fuzzy Set Theory, introduced by Zadeh in 1965 [77], has been the subject of much controversy and debate. In recent years, it has found many applications in a variety of fields. Among the most successful applications of this theory has been the area of Fuzzy Logic Control (FLC) initiated by the work of Mamdani and Assilian [36]. FLC has had considerable
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FUZZY LOGIC FOR CONTROL SYSTEMS
IFAC Proceedings Volumes, 1992Abstract Ever since fuzzy set theory was first proposed by Professor Zadeh, People in the control field have examined very carefully its applicability to the real world. Very few results of practical application were reported namely due to the difficulty of proving validity.
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