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

• 한국지능시스템학회논문지
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• 제11권9호
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• pp.833-836
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• 2001

공장 자동화 및 무인 자동화를 실현하기 위해 가장 기본적이며 중요한 기술은 제조공정에 대한 감시 기술이다. 특히 절삭공정에서 생산성을 향상시키기 위해서는 절삭 과정 중 드릴이나 앤드밀 등과 같은 공구의 마모상태가 실시간으로 감시되어야 한다. 본 논문은 드릴 공정에서 퍼지 논리를 도입하여 마모진단 시스템을 구성하였다. 실시간 마모진단을 위해서 절삭력과 절삭력의 변화량을 퍼지 입력 변수로 하여 컴퓨터를 이용한 드릴의 마모상태를 판단하는 알고리즘을 제안하였다. 제안된 퍼지 마모진단 시스템을 평가하기 위하여 퍼지 마모량과 드릴의 실제 마모량을 측정하여 그 결과를 비교하였다.

• 한국정밀공학회지
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• 제11권1호
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• pp.80-88
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• 1994

In the automation of rigid parts mating process with the intelligent robots, Peg-In-Hole is the most available task since inserting is some analytic and needs suitable range of forces that can be controlled by induatrial manipulators. In this Peg-In-Hole process, it is very important to identify the contact state between tow parts, peg and hole, to build the strategies for robot motion that leads to avoid the jamming condition occurs during insertion process. In this paper, we adpopted 3 parameters for identification, lFzl, lFxy/Fzl, and lMxy/Fxyl, derived from axes value of Whitney's jamming diagram. Also, we defined the fuzzy membership functions for these parameters and developed the identification algorithm based on fuzzy inference method of max-product. As an experimental result, we obtained about 96% of identification ratio that could be raised up to industrial requirements by further research.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.546-551
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• 2004

In this paper, a level controller is designed with the neuro-fuzzy model based on Takagi-Sugeno fuzzy system. The fuzzy system is employed as the controller, which can be tuned by the neural network mechanism based on a gradient descent technique. The tuning mechanism will provide an optimal process input by forcing the process error to zero. The proposed controller provides the online tunable mode to adjust the consequent membership function parameters. The controller is implemented with M-file and graphic user interface (GUI) of Matlab program. The program uses MPIBM3 interface card to connect with the industrial processes In the experimentation, the proposed method is tested to vary of the process parameters, set points and load disturbance. Processes of one tank and two tanks are used to evaluate the efficiency of our controller. The results of the both processes are compared with two PID systems that are 3G25A-PIDO1-E and E5AK of OMRON. From the comparison results, our controller performance can be archived in the case of more robustness than the two PID systems.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.95-98
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• 2003

In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's $\delta$$\^$2/ algorithm. The heart of this approach ls to reduce the computational burden during the FNN learning process and to improve convergence speed. The simulation results for nonlinear plant demonstrate the control effectiveness of the proposed system for optimal parameterization.

• 제어로봇시스템학회논문지
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• 제7권8호
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• pp.680-688
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• 2001

In this paper, the fuzzy control with variable input gain is applied to maintain the consistent tension in the process of taking up and releasing texture. In the process of discharging web on one side rolling it on another, the take-up drum gets smaller on the release drum side as it gets bigger on the rolling side, thus it is necessary to change the balance of velocity between the sides. In order to solve the problem a tension controller is necessary. The PI control method has been employed to maintain the consistent tension, but the PI control method produces a problem which requires an experienced worker with the traits of the machine, in order to perform the fine adjustments according to the environment of the process. For solving the above problem, we apply fuzzy control to the tension system, in order to produce a uniform roll. For the performance test, the fuzzy controller does not need to revise the parameters. Therefore the fuzzy controller exhibits an excellent additivity for the tension system where the system is changed with time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.183-188
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• 2005

Although it is widely used to find an optimum setting of manufacturing process parameters in a variety of engineering fields, the Taguchi method has a difficulty in dealing with multi-response situations in which several response variables should be considered at the same time. For example, electrode wear, surface roughness, and material removal rate are important process response variables in an electrical discharge machining (EDM) process. A simultaneous optimization should be accomplished. Many researches from various disciplines have been conducted for such multi-response optimizations. One of them is a fuzzy logic approach presented by Lin et al. [1]. They showed that two response characteristics are converted into a single performance index based upon fuzzy logic. However, it is pointed out that information regarding relative importance of response variables is not considered in that method. In order to overcome this problem, a desirability function can be adopted, which frequently appears in the statistical literature. In this paper, we propose a novel approach for the multi-response optimization by incorporating fuzzy logic into desirability function. The present method is illustrated by an EDM data of Lin and Lin [2].

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1510-1515
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• 2005

This paper is concerned with a Logic-based Fuzzy Neural Networks (LFNN) with the aid of fuzzy granulation. As the underlying design tool guiding the development of the proposed LFNN, we concentrate on the context-based fuzzy clustering which builds information granules in the form of linguistic contexts as well as OR fuzzy neuron which is logic-driven processing unit realizing the composition operations of T-norm and S-norm. The design process comprises several main phases such as (a) defining context fuzzy sets in the output space, (b) completing context-based fuzzy clustering in each context, (c) aggregating OR fuzzy neuron into linguistic models, and (c) optimizing connections linking information granules and fuzzy neurons in the input and output spaces. The experimental examples are tested through two-dimensional nonlinear function. The obtained results reveal that the proposed model yields better performance in comparison with conventional linguistic model and other approaches.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.97-100
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• 1997

In order to predict the moldability of a injection molded part, a simulation of filling is needed. Especially when short shot is predicted by CAE simulation in the filling stage, there are mainly three ways to solve the problem. Modification of gate and runner, replacement of plastic resin, and adjustment of process conditions are the main ways. Among them, adjustment of process conditions is the most economic way in the cost and time since the mold doesn\\`t need t be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, a fuzzy neural network(FNN) based upon injection molding process is proposed to evaluate moldability in filling stage and also to solve the problem in case of short shot. An adequate mold temperature is generated through the fuzzy neural network where fill time and melt temperature are taken into considerations because process conditions affect each other.

• 반도체디스플레이기술학회지
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• 제20권2호
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• pp.6-13
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• 2021

Cases and studies on the selection method of CTQ are relatively active, but there are few cases or studies on the selection method of CTP which is important in the device industry. In fact, many companies simply select and manage CTP from the point of contact based on their experience and intuition. The purpose of this study is to present an evaluation model and a mathematical decision model for rational and systematic CTP selection to improve the process quality of semiconductor equipment lines. In the evaluation model, AHP (Analytic Hierarchy Process) analysis technique was applied to show objective and quantitative figures, and Fuzzy decision-making model was used to solve the ambiguity and uncertainty in the decision-making process. Decision Value (DV) was presented. The subjects were 22 process factors managed in the Plating Process that the representative equipment line can do. As a result, the evaluation model proposed in this study can support more efficient and effective decision-making for process quality improvement by more objectively measuring the problem of subjective CTP selection in manufacturing sites.