• Korean Management Science Review
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• v.10 no.2
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• pp.1-9
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• 1993

This paper shows the application of fuzzy set and nonlinear membership function to linear programming problems in a fuzzy environment. In contrast to typical linear programming problems, the objectives and constraints of the problem in a fuzzy environment are defined imprecisely. This paper describes that fuzzy linear programming models can be formulated using the basic concepts of membership functions and fuzzy sets, and that they can be solved by quadratic programming methods. In a numerical example, a linear programming problem with two constraints and two decision variables is provided to illustrate the solution procedure.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.2
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• pp.13-23
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• 1994

This study proposed a method of accessment for traffic flow on roads based on the driver's decision making. In order to, an attempt is carried out to express driving speeds through driver's congnitive language theoretically and experimentally. Membership function is derived to express driver's congnitive language about driving speed through a fuzzy set theory and examines the applicability for speed evaluation. As a resul, the membership function of the recognized as medium by drivers almost agrees with the frequency distribution of speeds on roads constrtained at 50km/h.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.93.1-93
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• 2001

An evolutionary approach for the design of Fuzzy Logic Systems(FLSs) is proposed. Membership functions(MFs) in Takagi-Sugeno type fuzzy logic system is optimized through evolutionary process. Output singleton values are obtained through pseudo-inverse method. The proposed technique is unique for that, to prevent overfilling phenomenon, limited-level RBF membership functions are used and the new fitness function is invented. To show the effectiveness of the proposed method, some simulations results on model identification are given.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.772-781
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• 2009

This paper proposes a novel algorithm for the efficient classification and retrieval of medical images. After color and edge features are extracted from medical images, these two feature vectors are then applied to a multi-class Support Vector Machine, to give membership vectors. Thereafter, the two membership vectors are combined into an ensemble feature vector. Also, to reduce the search time, Correlated Categories Vector is proposed for similarity matching. The experimental results show that the proposed system improves the retrieval performance when compared to other methods.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.367-367
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of The Korean Astronomical Society
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• v.15 no.1
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• pp.1-7
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• 1982

The luminosity functions of 12 open clusters are derived for which their membership and the colors of their individual stars have been established by detailed proper motion study and high quality photometric work. The resulting luminosity functions of these clusters are presented and discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.162-165
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• 1996

In this paper, we proposed an optimization method of the membership function and the numbers of fuzzy rule base for the stabilization controller of the inverted pendulum system by genetic algorithm(GAs). Conventional methods to these problems need to an expert knowledge or human experience. The proposed genetic algorithm method will tune automatically the input-output membership parameters and will optimize their rule-base.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1996.10a
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• pp.211-216
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• 1996

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input-output pair. First, an architecture of fuzzy nerual networks with fuzzy weights and fuzzy biases is shown. Next a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value.A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.977.2-980
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• 1993

The fuzzifier circuit DPFC 7 is presented. Its features are: programmable membership function, CMOS digital interface, analog and current mode internal processing and integrability without external components. It has been designed to obtain a basic efficient block for fuzzy processing, to be included in a future architecture.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1191-1194
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• 1993

We consider a fuzzy controller corresponding to PI controller. This controller is applied to a controlled object which is a first order lag system with dead time. An antecedent part is divided into 3, 5, and 7 parts ( membership function of triangle shape ), and a consequent part into 3, 5, and 7 parts ( membership function of singleton ). In each combination of an antecedent part and a consequent one. We compare control efficiency under the performance criteria such that the overshoot is kept 20% and the ITAE index is minimized.