https://scholarworks.unist.ac.kr/handle/201301/141 Sun, 19 Apr 2026 08:10:03 GMT 2026-04-19T08:10:03Z https://scholarworks.unist.ac.kr/handle/201301/9219 Title: Design of Fuzzy Logic Controller with Inconsistent Rule Base Author(s): Yu, Wonseek; Bien, Zeungnam Abstract: As major functional units, the fuzzy logic controller (FLC) includes a fuzzy rule base and an inference engine. In constructing a fuzzy rule base, however, uncertainties and imprecision in the information about the controlled plant or in the extracted knowledge about actions of operators may result in inconsistent rules. But conventional inference methods for FLC often fail to handle such inconsistencies. In this article is proposed an effective method for obtaining a final conclusion from such inconsistent if-then rules. Also, as an alternative to conventional methods, a “minimum distance inference method” is applied for FLC. In the method, a metric is introduced to represent the distance between two fuzzy sets, and a new measure of certainty is used as a weight in the optimization to find a conclusion fuzzy set. The usefulness of the proposed methodologies is shown via a computer simulation of controlling a realistic overhead crane system Thu, 31 Dec 1992 15:00:00 GMT https://scholarworks.unist.ac.kr/handle/201301/9219 1992-12-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/9181 Title: DECENTRALIZED ITERATIVE LEARNING CONTROL METHODS FOR LARGE-SCALE LINEAR DYNAMIC-SYSTEMS Author(s): HWANG, DH; KIM, BK; Bien, Zeungnam Abstract: The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes Tue, 30 Nov 1993 15:00:00 GMT https://scholarworks.unist.ac.kr/handle/201301/9181 1993-11-30T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/9218 Title: 범용의 퍼지 하드웨어 설계 Author(s): Kim, Yong Tae; Lee, Seung Ha; Lee, Yun Jung; Bien, Zeungnam Abstract: Recently the fuzzy control is widely used as a tool for constructing automatic control systems which can replace the manual operation of large-scale nonlinear plants. In most applications of the fuzzy control however it is hard to meet the requirement of the operation time. In some real-time control the fuzzy control scheme requires too much computing time for fuzzification inference and defuzzification. To reduce the computing time there may be two alternatives the development of a new operation algorithm and the design of high-speed fuzzy hardware. In this paper to solve the problem of reducing the fuzzy operation time we propose a new high-speed fuzzy hardware scheme which has merits of its generality and extensibility. Finally we verify the proposed fuzzy hardware Fri, 31 Dec 1993 15:00:00 GMT https://scholarworks.unist.ac.kr/handle/201301/9218 1993-12-31T15:00:00Z https://scholarworks.unist.ac.kr/handle/201301/9217 Title: 화력 발전소 드럼형 보일러 시스템의 고장 진단을 위한 퍼지 전문가 시스템의 개발 Author(s): Bien, Seung Hyeon; Park, Se Hwa; Bien, Zeungnam Abstract: In this paper, a fuzzy expert system is developed for fault diagnoisis of a drum-type boiler system in fossil power plants. The develped fuzzy espert system is composed of knowledge base, fuzzification module, knowledge base process module, knowledge base management module, inference module, and linguistic approximation module. The main objective of the fuzzy expert system is to check the states of the system including the drum level and detect faults such as the feedwater flow sensor fault, feedwater flow control valve fault, and water wall bube rupture. The fuzzy expert system diagnoses faults using process values, manipulated values, and knowledge base which is built via interviews and questionaries with the experts on the plant operations. Finally, the validity of the developed fuzzy expert system is shown via experiments using the digital simulator for boiler system is Seoul Power Plant Unit 4 Fri, 31 Dec 1993 15:00:00 GMT https://scholarworks.unist.ac.kr/handle/201301/9217 1993-12-31T15:00:00Z