• 한국지능시스템학회논문지
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• 제14권6호
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• pp.776-782
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• 2004

Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

• 전자공학회논문지B
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• 제31B권5호
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• pp.149-161
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• 1994

The Max-Min CRI method , a traditional inference method , has three problems: subjective formulation of membership functions, error-prone weighting strategy, and inefficient compositional rule of inference. Because of these problems, there is an insurmountable error region between desired output and inferred output. To overcome these problems, we propose an Intelligent Neuro-Fuzzy System (INFS) based on fuzzy thoery and self-organizing functions of neural networks. INFS makes use of neural networks(Error Back Propagation) to solve the first problem, and NCRI(New Max-Min CRI) method for the second. With a proposed similarity measure, NCRI method is an improved method compared to the traditional Max-Min CRI method. For the last problem, we propose a new defuzzification method which combines only the appropriate rules produced by the rule selection level. Applying INFS to a D.C. series motor, we can conclude that the error region is reduced and NCRI method performs better than Max-Min CRI method.

• 물과 미래
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• 제19권4호
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• pp.339-346
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• 1986

소양강댐 및 충\ulcorner댐의 실시간 홍수조절 모형을 개발하기 위한 방법으로 Min-Max Dynamic Programming에 의한 최적화 기법을 사용하였다. 최적화 모형의 목적 함수로서는 각 댐의 최대 방류량을 최소화하도록 하였으며, 각 저수지 및 하도의 특성에 따른 제약 조건을 고려하였다. 개발된 단일 저수지 운영 모형에 의한 홍수조절 효과를 평가하는 척도로서 조절율과 이용율을 사용하였다. Technical ROM, Rigid ROM 및 Linear Decision rule과 같은 simulation 모형에 의한 조절 효과와 비교한 결과 모든 빈도에 대하여 DP에 의한 최적화 방법이 더 좋은 것으로 나타났다.

• 한국정밀공학회지
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• 제19권11호
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• pp.167-173
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• 2002

The look up table method conventionally applied to control the inner temperature and humidity of a laver drying machine has repeatedly occurred not only laver's damage but also inferior goods since the reaching time at the optimum state takes a long time. In this paper, a fuzzy control theory instead of the look up table was proposed to reduce the reaching time at the optimum state. The proposed method used six input variables and four output variables for the fuzzy control, and a triangle rule for a fuzzifier, The Mandani's min-max method was applied to a fuzzy inference. Also, the mean method of maximum was applied to a defuzzifier. The method applied to the fuzzy controller contributed to reduce the reaching time at the optimum state, and to minimize not only laver's damage but also inferior goods.

• 디지털콘텐츠학회 논문지
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• 제2권2호
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• pp.103-115
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• 2001

본 논문은 퍼지지식베이스에서 러프 집합과 요인공간이론을 적용하여 최소 결정규칙 생성과 근사추론 연산을 수행하는 두 개의 알고리듬을 제안한다. 최소 결정규칙의 생성은 속성요인에 관련한 상관분석과 베이지안 정리를 응용한 데이터의 분류기법과 리덕트에 의해 수행된다. 이 결정규칙으로 이루어진 최소지식 베이스의 탐색공간에서 소속함수와 t-norm의 합성 연산을 정의한 근사추론 방식에 의해 특정 객체를 검색한다. 본 연구의 러프와 퍼지연산 모듈을 수행하는 제안 알고리듬 기법을 객체및 속성수를 증가시키는 시뮬레이션을 통해 다른 검색이론 및 합성연산 방식과 비교하였다. 그 결과 다른 제 방법보다 본 연구에서 제안하는 기법이 특정 객체를 추출하기 위한 검색연산 시간에 있어 보다 빠르게 검색됨을 입증하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.975-976
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• 1993

This talk presents the overview of the author's research and development activities on fuzzy inference hardware. We involved it with two distinct approaches. The first approach is to use application specific integrated circuits (ASIC) technology. The fuzzy inference method is directly implemented in silicon. The second approach, which is in its preliminary stage, is to use more conventional microprocessor architecture. Here, we use a quantitative technique used by designer of reduced instruction set computer (RISC) to modify an architecture of a microprocessor. In the ASIC approach, we implemented the most widely used fuzzy inference mechanism directly on silicon. The mechanism is beaded on a max-min compositional rule of inference, and Mandami's method of fuzzy implication. The two VLSI fuzzy inference chips are designed, fabricated, and fully tested. Both used a full-custom CMOS technology. The second and more claborate chip was designed at the University of North Carolina(U C) in cooperation with MCNC. Both VLSI chips had muliple datapaths for rule digital fuzzy inference chips had multiple datapaths for rule evaluation, and they executed multiple fuzzy if-then rules in parallel. The AT & T chip is the first digital fuzzy inference chip in the world. It ran with a 20 MHz clock cycle and achieved an approximately 80.000 Fuzzy Logical inferences Per Second (FLIPS). It stored and executed 16 fuzzy if-then rules. Since it was designed as a proof of concept prototype chip, it had minimal amount of peripheral logic for system integration. UNC/MCNC chip consists of 688,131 transistors of which 476,160 are used for RAM memory. It ran with a 10 MHz clock cycle. The chip has a 3-staged pipeline and initiates a computation of new inference every 64 cycle. This chip achieved an approximately 160,000 FLIPS. The new architecture have the following important improvements from the AT & T chip: Programmable rule set memory (RAM). On-chip fuzzification operation by a table lookup method. On-chip defuzzification operation by a centroid method. Reconfigurable architecture for processing two rule formats. RAM/datapath redundancy for higher yield It can store and execute 51 if-then rule of the following format: IF A and B and C and D Then Do E, and Then Do F. With this format, the chip takes four inputs and produces two outputs. By software reconfiguration, it can store and execute 102 if-then rules of the following simpler format using the same datapath: IF A and B Then Do E. With this format the chip takes two inputs and produces one outputs. We have built two VME-bus board systems based on this chip for Oak Ridge National Laboratory (ORNL). The board is now installed in a robot at ORNL. Researchers uses this board for experiment in autonomous robot navigation. The Fuzzy Logic system board places the Fuzzy chip into a VMEbus environment. High level C language functions hide the operational details of the board from the applications programme . The programmer treats rule memories and fuzzification function memories as local structures passed as parameters to the C functions. ASIC fuzzy inference hardware is extremely fast, but they are limited in generality. Many aspects of the design are limited or fixed. We have proposed to designing a are limited or fixed. We have proposed to designing a fuzzy information processor as an application specific processor using a quantitative approach. The quantitative approach was developed by RISC designers. In effect, we are interested in evaluating the effectiveness of a specialized RISC processor for fuzzy information processing. As the first step, we measured the possible speed-up of a fuzzy inference program based on if-then rules by an introduction of specialized instructions, i.e., min and max instructions. The minimum and maximum operations are heavily used in fuzzy logic applications as fuzzy intersection and union. We performed measurements using a MIPS R3000 as a base micropro essor. The initial result is encouraging. We can achieve as high as a 2.5 increase in inference speed if the R3000 had min and max instructions. Also, they are useful for speeding up other fuzzy operations such as bounded product and bounded sum. The embedded processor's main task is to control some device or process. It usually runs a single or a embedded processer to create an embedded processor for fuzzy control is very effective. Table I shows the measured speed of the inference by a MIPS R3000 microprocessor, a fictitious MIPS R3000 microprocessor with min and max instructions, and a UNC/MCNC ASIC fuzzy inference chip. The software that used on microprocessors is a simulator of the ASIC chip. The first row is the computation time in seconds of 6000 inferences using 51 rules where each fuzzy set is represented by an array of 64 elements. The second row is the time required to perform a single inference. The last row is the fuzzy logical inferences per second (FLIPS) measured for ach device. There is a large gap in run time between the ASIC and software approaches even if we resort to a specialized fuzzy microprocessor. As for design time and cost, these two approaches represent two extremes. An ASIC approach is extremely expensive. It is, therefore, an important research topic to design a specialized computing architecture for fuzzy applications that falls between these two extremes both in run time and design time/cost. TABLEI INFERENCE TIME BY 51 RULES {{{{Time }}{{MIPS R3000 }}{{ASIC }}{{Regular }}{{With min/mix }}{{6000 inference 1 inference FLIPS }}{{125s 20.8ms 48 }}{{49s 8.2ms 122 }}{{0.0038s 6.4㎲ 156,250 }} }}

• 한국컴퓨터정보학회논문지
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• 제21권4호
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• pp.81-88
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• 2016

The optimal solution of quadratic assignment problem (QAP) cannot get done in polynomial time. This problem is called by NP-complete problem. Therefore the meta-heuristic techniques are applied to this problem to get the approximated solution within polynomial time. This paper proposes an algorithm for a random type QAP, in which the instance of two nodes are arbitrary. The proposed algorithm employs what is coined as a max flow-min distance rule by which the maximum flow node is assigned to the minimum distance node. When applied to the random type QAP, the proposed algorithm has been found to obtain optimal solutions superior to those of the genetic algorithm.

• 한국지능시스템학회논문지
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• 제5권4호
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• pp.67-76
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• 1995

퍼지 제어기에 관한 최근 연구에서, 연산의 간략성을 위해 퍼지 제어 규칙의 후건부에 대하여 퍼지 집합 대신에 크리스프 값을 사용하는 크리스프 타입 퍼지 제어기 모델이 다양한 분양의 응용에 널리 이용괴고 있다. 이 논문에서는 max-min 추론법 및 product-sum 추론법에 기초한 크리스프 타입 퍼지 제어기의 동특성을 해석하였다. 해석결과, 크리스프 타입 퍼지 제어기는 근사적으로 PD 제어기와 같이 동작함을 보였다.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2008년도 추계학술대회 논문집
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• pp.357-360
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• 2008

There are many control strategies for HEV in today. Expanding motor-driving and operating at good-efficient point in engine is the key of the HEV control to increase fuel economy. There are two types of HEV supervisory control. One is rule-based control and the other is optimization control. MAX-SOC control, thermostat control, baseline status control and state-machine control are in deterministic RBC. It is simple, but powerful and easy to apply in real-time circumstance. In this study, we analysis these four control strategies in RBC (Rule-based control) and identify the each advantage and disadvantage.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.911-914
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• 1993

This paper makes a trial to build the model of car-following in the state of starting to stable driving on the basic of driver's knowledge that is easily characterized by linguistical cognition. There are three main steps in building the model. Firstly, each driver's rule of three testees is studied in linguistical experssion by the interview and questionary surveys that are repeated once a day for ten days. Secondly, quantification of the linguistical expression is investigated by driving experiments that includes the questionary survey to the testee in the test vehicle, and the membership functions of variables of rule are obtained. Thirdly, implicaton and composition of fuzzy inference is made by Max-Min Methods and defuzzification by gravity method. It can be said that the proposed model of car-following based on driver's knowledge is practically allpicable to the estimation of drivering of car-following on trunk roads in urban area.