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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 5, Issue 4 - Dec 1995
Volume 5, Issue 3 - Sep 1995
Volume 5, Issue 2 - Jun 1995
Volume 5, Issue 1 - Mar 1995
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Fuzzy Almost Continuous Mappings and Fuzzy Almost Quasi-Compact Mappings
Park, Kuo-Duok ; Im, Young-Bin ; Soung, Goang-Ou ;
Journal of Korean Institute of Intelligent Systems, volume 5, issue 1, 1995, Pages 3~14
In this paper we introduce the class of fuzzy almost contrinuous mappings. It contains the class of fuzzy continuous mappings and is contained in the class of fuzzy weakly continuous mappings. In section 3 we discuss various properties of such mappings. In section 4 we also introduce the notion of fuzzy almost quasi-compact mappings and give relations between fuzzy almost quasi-compact mappings and the mapping which are introduced in section 2 and 3.
Properties of Triangle-Shaped Fuzzy Membership Function
Journal of Korean Institute of Intelligent Systems, volume 5, issue 1, 1995, Pages 15~20
Fuzzy membership functions are some kinds of mapping function for the fuzzification and the defuzzification. Triangle-shaped fuzzy membership functions are widely used in fuzzy controller, for it is easy to implement. In these membership functions, it is known that narrower fuzzy sets permit finer control near the operating point than that far from the operating point.
we have a membership function with narrower triangle near zero and wider triangle far from zero. The membership function will make fine control when small input is given and rough control at large input. Therefore the performance of the controller with that membership function will be enhanced. This paper presents how the width of triangle base in the fuzzy membership function has influence on the output using geometrical approaches.
Total Ordering by Fuzzy Inference
Hyung, Lee-Kwang ; Lee, Do-Heon ; Lee, Keon-Myung ;
Journal of Korean Institute of Intelligent Systems, volume 5, issue 1, 1995, Pages 21~24
Fuzzy inference method is introduced to order totally a partiallyordered system. When there there are more than one order indices and fuzzy order rules, the proposed method provides one order index by mixing them.
Design of FLC based on the concept of VSC for Home VCR Drum Motor
Park, Tae-Hong ; Lee, Sang-Lak ; Park, Gwi-Tae ; Lee, Kee-Samg ;
Journal of Korean Institute of Intelligent Systems, volume 5, issue 1, 1995, Pages 25~32
In this paper, the FLVSC (Fuzzy Logic Variable Structure controller), of which control rules are extracted from the concepts of the VSC(Variable Structure control) is proposed and diesgned for drum motor(BLDC motor) in home VCR. The FLC (Fuzzy Logic Controller) based on linguistic rules has the advantages of not needing of some exact mathermatical model for plant to be controlled. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of distrubances, parameter variations and uncertainites in a sliding mode. In addition, the method has the properties of the FLC-noise rejection capability etc. The computer simulation and experiment using DSP(TMS320C30) have been carried out for the servo control of VCR drum motor to show the usefulness of the proposed method.
Fuzzy-Neuro Controller for Control of Air-Conditioning System
Lee, Sang-Bae ;
Journal of Korean Institute of Intelligent Systems, volume 5, issue 1, 1995, Pages 33~42
A practical application of a fuzzy-neuro controller is described for an air-conditioning system. Air-handing units are being widely used for improving the performance of central air-conditioning systems. The fuzzy-neuro control system has two controlled variables, temperature and humidity and three control elements, cooling, heating, and humidification. In order to achieve high efficiency and economical contorl, especially in large offices and industrial buildings, two controllable parameters, temperature and humidity, must be adequately controlled by the three final controlling elements. In this paper a fuzzy-neuro control system is described for controlling air-conditioning systems efficiently and economically. Simulation results confirmed that the fuzzy neuro control system is effective for this multivariable system.
Development of a Neural Network with Fuzzy Preprosessor
Journal of Korean Institute of Intelligent Systems, volume 5, issue 1, 1995, Pages 43~51
In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classifi¬cation accuracy but also for being able to classify objects whose attribute values do not have clear bound¬aries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. 'The transformed input is processed in the postprocessing module. The experimental results indi-cate the superiority of fuzzy input signal representation scheme in comparison to binary input signal rep¬resentation scheme and decimal input signal representation scheme
On Developing The Intellingent contro System of a Robot Manupulator by Fussion of Fuzzy Logic and Neural Network
Journal of Korean Institute of Intelligent Systems, volume 5, issue 1, 1995, Pages 52~64
Robot manipulator is a highly nonlinear-time varying system. Therefore, a lot of control theory has been applied to the system. Robot manipulator has two types of control; one is path planning, another is path tracking. In this paper, we select the path tracking, and for this purpose, propose the intelligent control¬ler which is combined with fuzzy logic and neural network. The fuzzy logic provides an inference morphorlogy that enables approximate human reasoning to apply to knowledge-based systems, and also provides a mathematical strength to capture the uncertainties associated with human cognitive processes like thinking and reasoning. Based on this fuzzy logic, the fuzzy logic controller(FLC) provides a means of converhng a linguistic control strategy based on expert knowledge into automahc control strategy. But the construction of rule-base for a nonlinear hme-varying system such as robot, becomes much more com¬plicated because of model uncertainty and parameter variations. To cope with these problems, a auto-tuning method of the fuzzy rule-base is required. In this paper, the GA-based Fuzzy-Neural control system combining Fuzzy-Neural control theory with the genetic algorithm(GA), that is known to be very effective in the optimization problem, will be proposed. The effectiveness of the proposed control system will be demonstrated by computer simulations using a two degree of freedom robot manipulator