• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1950-1955
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• 1991

A fuzzy dynamic learning controller is proposed and applied to control of time delayed, non-linear and unstable chemical processes. The proposed fuzzy dynamic learning controller can self-adjust its fuzzy control rules using the external dynamic information from the process during on-line control and it can create th,, new fuzzy control rules autonomously using its learning capability from past control trends. The proposed controller shows better performance than the conventional fuzzy logic controller and the fuzzy self organizing controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.219-223
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• 2009

Recently, Zhao et al. [Fuzzy Optimization and Decision Making (2007) 6, 279-295] characterized the interarrival times as fuzzy random variables and presented a fuzzy random elementary renewal theorem on the limit value of the expected renewal rate of the process in the fuzzy random renewal process. They also depicted both the interarrival times and rewards are depicted as fuzzy random variables and provided fuzzy random renewal reward theorem on the limit value of the long run expected reward per unit time in the fuzzy random renewal reward process. In this note, we simplify the proofs of two main results of the paper.

• Journal of applied mathematics & informatics
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• 제27권5_6호
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• pp.1459-1463
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• 2009

Recently, Zhao et.al [European Journal of Operational Research 169 (2006) 189-201] discussed a random fuzzy renewal process based on random fuzzy theory. They considered the rate of the random fuzzy renewal process and presented a random fuzzy elementary renewal theorem. They also established Blackwell's theorem in random fuzzy sense. But all these results are invalid. We give a counter example in this note.

• 한국정밀공학회지
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• 제15권2호
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• pp.75-80
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• 1998

Process information is known to have the continuous distribution in many manufacturing processes. Generalized p-chart has been developed for controlling processes by classifying the information characteristics into several groups. But it is improper to describe continuous processes with the classified process informal ion, which is based on the classical set concept. Fuzzy control chart, has been developed for the control of linguistic data, but it is also based on the dichotomous notion of classical set theory. In this paper, fuzzy sampling method is studied in order to process the uncertain data properly. The method is incorporated with the fuzzy control chart. Statistical characteristics of the fuzzy representative value are utilized to device the fuzzy-statistical control chart. The fuzzy-statistical control chart is compared with the generalized p-chart and both the sensitivity to the process information distribution change pared robustiness against the noise on the process information of the fuzzy-statistical control chart are shown to be superior.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.84-89
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• 2003

Fuzzy controller is one of the succeed controller used in the process control in case of model uncertainties. But it my be difficult to fuzzy controller to articulate the accumulated knowledge to encompass all circumstance. Hence, it is essential to provide a tuning capability. There are many parameters in fuzzy controller can be adapted, scale factor tuning of normalized fuzzy controller is one of the adaptation parameter. Two adaptation methods are implemented in this work on an experimental thermal process, which simulate heating process in liquefied petroleum gases (LPG) recovery process in one of petrochemical industries: Gradient decent (GD) adaptation method; supervisory fuzzy controller. A comparison between the two methods is discussed.

• 한국철도학회논문집
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• 제9권4호
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• pp.432-440
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• 2006

Prior to the service evaluation, many kinds of its attributes must be identified on the basis of rational decision owing to complexity and ambiguity inherent in logistics service. there are so many evaluation methods but they are not applicable to logistics service the have property of non-additivity and overlapped attributes. Therefore, probability measure can not used to evaluate logistics service but Fuzzy Measure is required. And the method should be easy to calculate it Recently Fuzzy theory has been applied in Various evaluation problem. Fuzzy evaluation based on Fuzzy theory can accommodate fuzziness in judgement with people through introducing Fuzzy Measure. In this paper, Hierarchical Fuzzy Process is applied to evaluate level of logistics service in Korail and the biggest six logistics companies in the korea which is called 3PL Company. Also Fuzzy Relation Equation which is problem identifying evaluation value at these fuzzy evaluation is applied to verify relation between Input and Output data through @-operation. Therefore, we apply this Fuzzy Relation Equation to Hierarchical Fuzzy Process and verify evaluation value which objects of evaluation are able to possess.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2000년도 추계학술대회논문집
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• pp.25.2-31
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• 2000

Recently, Fuzzy theory has been applied in evaluation problem. Fuzzy evaluation based on Fuzzy theory can accommodate fuzziness of judgement with people through introducing Fuzzy measure. Representative Fuzzy evaluation is Fuzzy Integral using Fuzzy measure. Definite methodology using Fuzzy Integral HFI(Hierarchical Fuzzy Integrals), HFEA(Hierarchical Fuzzy Evaluation Algorithm), HFP(Hierarchical Fuzzy Process), etc. In this paper, we deal with problem identifying evaluation value using Fuzzy Relation Equation at these Fuzzy evaluation. We verify relation between Input data and Output data through @-operation and apply this to HFP. And that we verify evaluation value which objects of evaluation are able to possess.

• 전자공학회논문지B
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• 제33B권12호
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• pp.84-94
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• 1996

In this work, a fuzzy petri net model for modeling a general form of fuzzy production system which consists of chaining fuzzy production rules and so requires multistage reasoning process is presented. For the obtained fuzzy petri net model, the net will be transformed into some matrices, and also be systematically led to an algebraic form of a state equation. Since it is fond that the approximate reasoning process in fuzzy systems corresponds to the dynamic behavior of the fuzzy petri net, it is further shown that the multistage reasoning process can be carried out by executing the state equation.

• International journal of advanced smart convergence
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• 제2권1호
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• pp.12-17
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• 2013

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the parameters of the consequence part are estimated by least square errors. The proposed model is evaluated with the performance using the data widely used in nonlinear process. Finally, this paper shows that the proposed model has the good result for high-dimension nonlinear process.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.104-111
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• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.