The paper presents a Genetic Algorithm(GA)-based system for online acquisition and modification of rules for a fuzzy logic controller. This uses a version of the rule competition production systems, called Classifier ...
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The paper presents a Genetic Algorithm(GA)-based system for online acquisition and modification of rules for a fuzzy logic controller. This uses a version of the rule competition production systems, called Classifier systems, but in which the rules are matched in the fuzzy domain rather than as binary patterns. The GA is operated in the incremental mode whereby only one structure from a population is evaluated in each time interval. To hasten the learning process, the payoff received is used to assign estimates of new strengths to the other classifiers, dependent on the degree of matching with the evaluated classifier. The rule learning is initialized with randomly generated structures to which fairly general heuristic knowledge has been added. The interacting environment has been modelled by a real time simulation of closed loop administration of an anaesthetic drug, but the characteristics of the environment are not known to the GA.
The paper presents a Genetic Algorithm(GA)-based system for online acquisition and modification of rules for a fuzzy logic controller. This uses a version of the rule competition production systems, called Classifier ...
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The paper presents a Genetic Algorithm(GA)-based system for online acquisition and modification of rules for a fuzzy logic controller. This uses a version of the rule competition production systems, called Classifier systems, but in which the rules are matched in the fuzzy domain rather than as binary patterns. The GA is operated in the incremental mode whereby only one structure from a population is evaluated in each time interval. To hasten the learning process, the payoff received is used to assign estimates of new strengths to the other classifiers, dependent on the degree of matching with the evaluated classifier. The rule learning is initialized with randomly generated structures to which fairly general heuristic knowledge has been added. The interacting environment has been modelled by a real time simulation of closed loop administration of an anaesthetic drug, but the characteristics of the environment are not known to the GA.
The authors describe how fuzzy patterns captured by tactile sensing of hardness features of a coal seam can be processed and used for the steering of rock-cutting mining machines in geological environments. A method o...
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The authors describe how fuzzy patterns captured by tactile sensing of hardness features of a coal seam can be processed and used for the steering of rock-cutting mining machines in geological environments. A method of simulating geologically reasonable coal seam hardness images with controlled induction of noise is presented. A simple algorithm to improve the tracking of coal seam hardness profile is described. Using the simulated hardness pattern, the applicability and effectiveness of this algorithm is demonstrated.< >
The authors have been working on the use of AI techniques to support modelling and simulation of dynamic systems and have developed a demonstration environment KEMS (Knowledge-based Environment for Modelling and Simul...
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The authors have been working on the use of AI techniques to support modelling and simulation of dynamic systems and have developed a demonstration environment KEMS (Knowledge-based Environment for Modelling and Simulation) which is based on the idea of re-usable components. Once the structure of the model is established, KEMS generates automatically the simulation code. The fundamental ideas in KEMS arise from consideration of methodologies for applying AI to the construction of models and hence they have also considered how the techniques can be applied to model-based software engineering methodologies for specification and design of software. They discuss the structure of KEMS, knowledge acquisition and the application to software specification and construction using MASCOT.< >
作者：
Theocharis, J.Petridis, V.Dr.-Ing. John Theocharis (1956) graduated as an Electrical Engineer from Aristotelian University of Thessaloniki.Greece
in 1980. From 1980 to 1985 he has been with the scientific staff of the Department of Electrical Engineering at the Aristotelian University where he received the Ph.D. degree in 1985. Since 1986 he is working as a lecturer and in 1990 he became assistant professor at the Department of Electronics and Computer Engineering in the m e university. His research activities include control power electronics and electrical motor drives. Recently he is working with the Neural Network Systems with applications to field oriented control problems. Aristotelian University of Thessaloniki School of Engineering Faculty of Electrical Engineering Dept. of Electronics & Computer Engineering P.O. Box 438 GR-Thessalonikil/Greece.T+3131/219784Fax + 3031/274868) Prof. Dr.-Ing. Vasilis Petridis (1946) graduated from National Technical University of Athens
Greece in 1969.He obtained the M.Sc. and the Ph.D. in electronics and systems from the University of London in 1970 and 1974. respectively. H i s interests include applied automatic control neural networks drives dynamic systems robotics etc. He is currently professor in the Department of Electronics and Computer Engineering of the University of Thessaloniki. (Aristotelian University of Thessaloniki. School of EngineeringFaculty of Electrical Engineering Dept. of Electronics & Computer Engineering P.O. Box 438. GR-ThessaloniW Greece T+3031/219784.Fax+3031/274868)
The procedure of harmonic insertion is generalized in this paper. Analytical expressions of the voltage spectra are derived. The insertion of the 3rd harmonic to the modulating signal, which is of particular interest,...
Taking into account the fuzzy nature of human decision making processes and real-time properties, this paper has established a unified approximate reasoning model based on Possibility Theory rather than on Relational ...
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Knowledge-based control based on fuzzy logic has been applied to many situations, but suffers from a number of disadvantages. These include the difficulty of rule-base selection, scaling factors selection and computin...
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Knowledge-based control based on fuzzy logic has been applied to many situations, but suffers from a number of disadvantages. These include the difficulty of rule-base selection, scaling factors selection and computing burden. One approch to overcome rule-base specificity is to use the concept of Self-Organizing Fuzzy Logic control (SOFLC), but this further increases the computing intensity. SOFLC has been investigated for slow-speed medical systems (Linkens and Hasnain, 1990). In this paper a real-time SOFLC for industrial processes is described. Two kinds of laboratory scale real processes were chosen, first a low speed liquid level rig using sequential programming techniques, and the second one is a fast multi-variable electric drive process using parallel processing techniques.
Taking into account the fuzzy nature of human decision making processes and real-time properties, this paper has established a unified approximate reasoning model based on Possibility Theory rather than on Relational ...
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Taking into account the fuzzy nature of human decision making processes and real-time properties, this paper has established a unified approximate reasoning model based on Possibility Theory rather than on Relational Matrix Computation. In the case of sensor-based situations, a more simple reasoning scheme is derived by introducing the concepts of matching measures. Proposed models may provide another possibility for on-line reasoning in real-time expert control system applications.
Growing complexity in gas turbine engines and the demand for improved performance suggest that parallel processing systems will be necessary to meet processing demands. These demands are further heightened by the need...
Growing complexity in gas turbine engines and the demand for improved performance suggest that parallel processing systems will be necessary to meet processing demands. These demands are further heightened by the need for complex control and health monitoring functions and a software fault tolerance capacity. Using the Inmos transputer as the processing element for the parallel system, this paper explores the potential of such a system to meet these demands with reference to a specific practical turbine engine example. Mapping issues are explored and real-time simulations demonstrate the validity of the approach, while highlighting difficulties associated with the parallelization of irregularly structured systems.
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