• Journal of KIISE:Software and Applications
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• 제32권9호
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• pp.927-935
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• 2005

The certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions can be represented by intervals, such as vague numbers between zero and one based on vague sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner[18]. we are also proposed an efficient algorithm to perform vague set reasoning automatically. This vague set reasoning algorithm allows the rule-based systems to perform reasoning in a more flexible and more efficient.

• Journal of the Korean Institute of Intelligent Systems
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• 제16권3호
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• pp.291-296
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• 2006

In context-aware computing environment, some context is characterized by a single event, but many other contexts are determined by a sequence of events which happen with some timing constraints. Therefore context inference could be conducted by monitoring the sequence of event occurrence along with checking their conformance with timing constraints. Some context could be described with fuzzy concepts instead of concrete concepts. Multiple entities may interact with a service system in the context-aware environments, and thus the context inference mechanism should be equipped to handle multiple entities in the same situation. This paper proposes a context inference model which is based on the so-called fuzzy colored timed Petri net. The model represents and handles the sequential occurrence of some events along with involving timing constraints, deals with the multiple entities using the colored Petri net model, and employs the concept of fuzzy tokens to manage the fuzzy concepts.

• 전기의세계
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• 제39권8호
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• pp.7-12
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• 1990

전문가 시스템에 있어서의 불확실성 정보의 표현 및 처리를 담당하는 주요 추론모델중 Bayesian모델, Certainty Factor 모델 그리고 Dempster-Shafer 모델의 기본이론을 살펴보고자 한다. 이외의 주요 추론 방법으로서 Fuzzy추론 모델이 있는데 이는 판단 지식에 대한 주관적 불확실성과 "매우", "많이" 등의 자연어가 포함하고 있는 불분명성을 체계적이고 효과적으로 다룰 수 있는 Fuzzy Set 이론에 근거한 방법으로서, 불확실성 또는 불명료성을 0에서부터 1 사이의 값을 갖는 membership degree로 표시하며 이를 "MIN"과 "MAX" 함수를 이용한 합성 추론 규칙(Composition Rule of Inference)를 적용하여 처리한다. Fuzzy 추론 모델은 자연어를 포함하는 전문가의 지식 처리에 매우 적합하여 앞으로 그 응용이 높이 기대되는 방법이다. 이외에 Bayesian 모델을 변형 응용한 PROSPECTOR의 Likelyhood Ratio 모델, 정량적 방법인 Theory of Endorsement 모델 등 여러 방법이 있다. 그러나 어느 모델이 더 일반성을 갖고 더 좋은 방법인가 하는 문제에 대하여는 아직 많은 연구가 요구된다. 따라서 이러한 모델들의 전문가 시스템 적용에 있어서는 각 모델의 장단점을 고려하여 주어진 문제 영역에 적합한 모델을 선택하는 것이 바람직하다. 현재 불확실성 처리에 있어서 각 문제에 따른 경험적인 처리에 의존하는 전력 계통 분야의 적용에 있어서도 이러한 실인간 전문가의 추론방법에 근접된 반성을 갖는 불확실성 추론 방버 도입이 요구된다.가의 추론방법에 근접된 반성을 갖는 불확실성 추론 방버 도입이 요구된다.

• Journal of Korea Society of Industrial Information Systems
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• 제6권1호
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• pp.61-69
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• 2001

This study concerns designing effective fuzzy inference rules, which can be used to evaluate other experiment sets for sensory tests. The number of fuzzy inference rules might be determined by the fuzzy division of variables. For the more the number of fuzzy division does not mean the more effectiveness, the number of inference rules should be reduced to improve efficiency of inference engine of expert system. This study verified that its suggested method and inference rules are effective in comparison with the existing studies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제7권3호
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• pp.132-136
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• 2014

We introduce a new architecture of TSK-based fuzzy inference system. The proposed model used fuzzy c-means clustering method(FCM) for efficient disposal of data. The premise part of fuzzy rules don't assume any membership function such as triangular, gaussian, ellipsoidal because we construct the premise part of fuzzy rules using FCM. As a result, we can reduce to architecture of model. In this paper, we are able to use four types of polynomials as consequence part of fuzzy rules such as simplified, linear, quadratic, modified quadratic. Weighed Least Square Estimator are used to estimates the coefficients of polynomial. The proposed model is evaluated with the use of Boston housing data called Machine Learning dataset.

• The Journal of the Korea Contents Association
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• 제11권3호
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• KSCI Review
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• 제2권2호
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• pp.51-56
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• 1996

Fuzzy 이론은 인간의 행동, 인식등에 관한 정량적인 측정이 어려운 정보를 분석하고, 의사 결정을 행하는 이론으로서, 최근에는 일상 생활의 여러 분야에서 많이 응용되고 있는 이론이다. 본 논문에서는 Fuzzy 이론중 Fuzzy 추론을 이용하여 운전자의 심리 상태를 Fuzzy 합성을 통하여, Mamdai의 Min-Max 중심법을 사용하여 분석하였다.

• Journal of the Korean Institute of Intelligent Systems
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• 제25권1호
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• pp.57-62
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• 2015

In this paper, we design fuzzy inference-based deterioration diagnosis system through different image for rapid as well as efficient diagnosis of electrical equipments. When the deterioration diagnosis of the electrical equipment starts, abnormal state of assigned area is detected by comparing with the temperature of the first normal state of the area. Deterioration state of detected area is diagnosed by using fuzzy inference algorithm. In the fuzzy inference algorithm, fuzzy rules are defined by If-then form and are described as look-up table. Both temperature and its ensuing variation are used as input variables. While triangular membership function is used for the fuzzy input variables of fuzzy rules, singleton membership function is used for the output variable of fuzzy rules. The final output is calculated by using the center of gravity of fuzzy inference method. Experimental data acquired from individual electrical equipments is used in order to evaluate the output performance of the proposed system.

• Journal of the Korea Society of Computer and Information
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• 제16권1호
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• pp.71-80
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• 2011

This paper provides a novel approach for developers to use face detection techniques for their applications easily without special knowledge by selecting optimal face detection algorithms based on fuzzy inference. The purpose of this paper is to come up with a high-level system for face detection based on fuzzy inference with which users can develop systems easily and even without specific knowledge on face detection theories and algorithms. Important conditions are firstly considered to categorize the large problem space of face detection. The conditions identified here are then represented as expressions so that developers can use them to express various problems. The expressed conditions and available face detection algorithms constitute the fuzzy inference rules and the Fuzzy Interpreter is constructed based on the rules. Once the conditions are expressed by developers, the Fuzzy Interpreter proposed take the role to inference the conditions and find and organize the optimal algorithms to solve the represented problem with corresponding conditions. A proof-of-concept is implemented and tested compared to conventional algorithms to show the performance of the proposed approach.

• Journal of Biomedical Engineering Research
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• 제18권4호
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• pp.491-498
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• 1997

This paper describes the arousal measurement and control system using fuzzy logic to prevent drowsy driving. Sugeno's method was used for fuzzy inference in this study. Arousal evaluation and control criteria were modified from result of Nz-IRI analysis depending on arousal sate. Membership function and rule base of fuzzy inference were determined from the modified arousal level criteria When lRl (Inter-SIR Interval) was shorter than 60sec, outputs of both methods were changed from small to big, but output of three step warning method was same level until the next warning range. Since output of fuzzy inference tracked well the change of subject's arousal level, problems of three step warning method could be overcome by fuzzy inference method Furthermore, the output of the fuzzy inference was highly correlated with Nz(r = 0.99). Therefore, the fuzzy inference method for evaluation and the control of arousal will be more effective at real driving situation than three step warning method.