• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.169-172
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• 2001

In recent years, many studies regarding the modeling of fuzzy system have been conducted. In this paper, a new computational approach to modeling of linguistic fuzzy system is proposed The fuzzy system is modeled as a combination of affine systems, The proposed method can be used in a rigorous stability analysis of fuzzy system including the linguistic fuzzy controller.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1999.11a
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• pp.119-124
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• 1999

A selection methodology of liquid levelmeters, especially, level sensors in non-nuclear category, to be installed in nuclear energy facilities is developed using linguistic fuzzy approaches such as fully-linguistic and semi-linguistic methods. Depending on defuzzification techniques, the linguistic fuzzy methodology leads to either linguistic (exactly, fully-linguistic) or cardinal (i.e., semi-linguistic) evaluation. For the linguistic method, for each alternative, fuzzy preference index is converted to linguistic utility value by means of a similarity measure determining the degree of similarity between fuzzy index and linguistic ratings. For the cardinal method, the index is translated to cardinal overall utility value. According to these values, alternatives of interest are linguistically or numerically evaluated and a suitable alternative can be selected. Under given selection criteria, the suitable selections out of some liquid levelmeters for nuclear facilities are dealt with using the linguistic fuzzy methodology proposed. Then, linguistic fuzzy evaluation results are compared with qualitative result available in the literature. It is found that as to a suitable option the linguistic fuzzy selection is in agreement with the qualitative selection. Additionally, the comparative study shows that the fully-linguistic method using adequate scale system facilitates linguistic interpretation regarding evaluation results.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.89-97
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• 2001

Chen and Chen(FSS, 1993, 159-168) presented a reasonable analytical model of fuzzy closed-loop systems and proposed a method to analyze the stability of fuzzy control by the relational matrix of fuzzy system. Chen, Lu and Chen(IEEE Trans. Syst. Man Cybern., 1995, 881-888) formulated the sufficient and necessary conditions on stability of fuzzy closed-loop control systems. Gang and Chen(FSS, 1996, 27-34) deduced a linguistic relation model of a fuzzy closed loop control system from the linguistic models of the fuzzy controller and the controlled process and discussed the linguistic stability of fuzzy closed loop system by a linguistic relation matrix. In this paper, we study more on their models. Indeed, we prove the existence and uniqueness of equilibrium state $X_e$ in which fuzzy system is stable and give closed form of $X_e$. The same examples in Chen and Chen and Gang and Chen are treated to analyze the stability of fuzzy control systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1309-1312
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• 1993

We present a flexible retrieval system of face photographs based on their linguistic descriptions in terms of fuzzy perdicates. While natural for describing a face, linguistic expressions are also subjective, which affects the retrieval result. Thus, the capability of a retrieval system to adjust to different users becomes very important. In this research we use fuzzy logic techniques, for describing image data, inference for retrieval and adjustment to a new user. Experimental results of the adjustment are also included.

• Journal of KSNVE
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• v.6 no.5
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• pp.547-554
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• 1996

Diagnosis is one of the dominant applications of expert systems technology today. Most diagnosis system is apply to if-then rule, and it is called production systems which consist of linguistic data. A new diagnosis method is suggested in this paper, in which the fuzzy reasoning theory is used to diagnosis the rotating machinery. Diagnosis algorithm is made fuzzy reasoned by using linguistic data of fuzziness. Linguistic data for fuzziness was described in fuzzy scale and fuzzy membership function. Then, those lingnistic data have been synthesized and defuzzificated according to every item observed. This system is successfully used for linguistic data in fuzziness of rotating machinery. The results indicate that the realistic application can be built in precision diagnosis system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1082-1085
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• 1993

In this paper, we notice the fact that a human learning process is characterized by a process under a natural language environment, and discuss an approach of learning based on indirect linguistic instructions. An instruction is interpreted through some meaning elements and each trend. Fuzzy evaluation rule are constructed for the searched meaning elements of the given instruction, and the performance of a system to be learned is improved by the evaluation rules. In this paper, we propose a framework of learning based on indirect linguistic instruction based learning using fuzzy theory: FULLINS(FUzzy-Learning based on Linguistic IN-Struction). The validity of FULLINS is shown by applying it to helicopter flight control.

• Journal of the Korean Institute of Navigation
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• v.8 no.1
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• pp.15-42
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• 1984

Many studies have been done in the field of fuzzy logic theory, but it's application to the ship's steering system is few until this date. This paper is to survey the effect of application of fuzzy logic control by new compositional rule of Inference to the ship's steering system. The controller is made up of a set of Linguistic Control Rules which are conditional linguistic statements connecting the inputs and output, and take the inputs derived from deviation angle and it's angular velocity. The Linguistic Control Rules are implemented on the digital computer to verify the performance of the fuzzy logic controller and simulations have been done in six cases of initial condition and disturbance type. Consequently, it was proved that the ship's steering system by introducing the F.L.C. is performed efficiently and less energy loss system compared with the conventional autopilot.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.827-832
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• 2001

In this paper, we propose a sequentially optimization method for fuzzy inference system using fuzzy equalization and linguistic hedge. The fuzzy equalization does not require the usual learning step for generating fuzy rules. However, it is too sensitive for the given input-output data set. So, we adopt a sequential scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership function obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to rice taste data and got better results than previous ones.

• Journal of the Korean Institute of Navigation
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• v.5 no.2
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• pp.59-88
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• 1981

Many studies have been done in the field of fuzzy logic theory, but it's application is not so much, and particularly, there isn't any application to the ship's steering system, until now. This paper is to survey the effect of application of fuzzy logic control to the ship's steering system. The controller is made up of a set of Linguistic Control Rules which are conditional linguistic statements connecting the inputs and the output, and take the inputs derived from the errors, that is, deviation angle and it's angular velocity. These two variables together give information about the state of the steering system, and the Linguistic Control Rules are implemented on the digital computer. The characteristics of this system were investigated through the computer simulation and satisfactory results compared with that of the conventional PD controller were obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.97-101
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• 1997

In recent years, there have been a number of applications of fuzzy logic in fuzzy reasoning system. The main objective of these applications is to approximate a decision making using the fuzzy reasoning system. This paper designs a fuzzy reasoning model for the decision making of registration fee at a private school, implements it applying for linguistic variables and fuzzy rules, and evaluates the practical availability of the model. The system accepts fuzzy rules, the type of membership functions, the domain of fuzzy sets and hedge, and fuzzifies the linguistic variables to generates fuzzy sets. The fuzzy sets generated are combined to constructs a solution fuzzy set. Finally, the system defuzzifies the solution fuzzy set to calculate a scalar value which is used for decision making.