• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.161.4-161
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• 2001

This paper presents adaptive control of robot manipulator using neuro-fuzzy controller Fuzzy logic is control incorrect system without correct mathematical modeling. And, neural network has learning ability, error interpolation ability of information distributed data processing, robustness for distortion and adaptive ability. To reduce the number of fuzzy rules of the FLS(fuzzy logic system), we consider the properties of robot dynamic. In fuzzy logic, speciality and optimization of rule-base creation using learning ability of neural network. This paper presents control of robot manipulator using neuro-fuzzy controller. In proposed controller, fuzzy input is trajectory following error and trajectory following error differential ...

• Journal of Advanced Marine Engineering and Technology
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• 제28권5호
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• pp.793-800
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• 2004

The purpose of this study is to develope a servo system with faster and more accurate response. This paper describes a method of approximate reasoning for fuzzy control of servo system based on the decomposition of $\alpha$-level fuzzy sets. We propose that fuzzy logic algorithm is a body from fuzzy inference to defuzzificaion cases where the output variable u directly is generated PWM The effectiveness for robust and faster response of the fuzzy control scheme are verified for a variable parameter by comparison with a PID control and fuzzy control A position control of DC servo system with a fuzzy logic controller is demonstrated successfully.

• 한국시뮬레이션학회논문지
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• 제17권3호
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• pp.9-18
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• 2008

단순히 퍼지만을 사용하여 시스템을 연동하는 경우와 퍼지로직을 같이 사용하여 침입 탐지 에이전트의 시스템 성능을 향상시키는 경우에 관한 연구로서, 블랙보드 기반의 비퍼지로직을 사용하는 경우와 블랙보드 기반의 퍼지 로직을 사용하는 경우을 비교한다. 또한 BBA를 통해 정적으로 대응하던 시스템을 향상시켜 동적 대응이 가능하게 구성하여 현실적인 시스템이 되도록 구성하였다. 대상 시스템의 성능을 평가하기 위하여 시뮬레이션을 수행하였다. 퍼지 시스템을 사용함으로써 false negative를 줄일 수 있었다. 분산 침입탐지를 위해 포함된 퍼지로직은 다양한 요소를 고려하기 때문에 침입의 성능을 높일 수 있다. 퍼지시스템을 사용하는 경우와 비 퍼지 시스템의 성능을 비교함으로써 퍼지 시스템의 성능 향상을 보이며, 이러한 비교를 통해 전체 시스템의 성능 향상을 보인다.

• 논리연구
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• 제11권1호
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• pp.131-156
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• 2008

This paper first investigates several uninorm logics (introduced by Metcalfe and Montagna in [8]) as fuzzy-relevance logics. We first show that the uninorm logic UL and its extensions IUL, UML, and IUML are fuzzy-relevant; fuzzy in Cintula's sense, i.e., the logic L is complete with respect to linearly ordered L-matrices; and relevant in the weak sense that ${\Phi}{\rightarrow}{\Psi}$ is a theorem only if either (i) $\Phi$ and $\Psi$ share a sentential variable or constant, or (ii) both $\sim\Phi$ and $\Psi$ are theorems. We next expand these systems to those with $\triangle$.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• Smart Structures and Systems
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• 제31권2호
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• pp.199-212
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• 2023

Nowadays fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages. Generalized type-2 fuzzy sets allow for a third dimension to capture higher order uncertainty and therefore offer a very powerful model for uncertainty handling in real world applications. With the recent advances that allowed the performance of general type-2 fuzzy logic controllers to increase, it is now expected to see the widespread of type-2 fuzzy logic controllers to many challenging applications in particular in problems of structural control, that is the case study in this paper. It should be highlighted that this is the first application of general type-2 fuzzy approach in civil structures. In the following, general type-2 fuzzy logic controller (GT2FLC) will be used for active control of a 9-story nonlinear benchmark building. The design of type-1 and interval type-2 fuzzy logic controllers is also considered for the purpose of comparison with the GT2FLC. The performance of the controller is validated through the computer simulation on MATLAB. It is demonstrated that extra design degrees of freedom achieved by GT2FLC, allow a greater potential to better model and handle the uncertainties involved in the nature of earthquakes and control systems. GT2FLC outperforms successfully a control system that uses T1 and IT2 FLCs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권3호
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• pp.171-177
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• 2011

The decision making process is based on accurate and timely available information. To obtain precise information from the internet is becoming more difficult due to the continuous increase in vagueness and uncertainty from online information resources. This also poses a problem for blind people who desire the full use from online resources available to other users for decision making in their daily life. Ontology is considered as one of the emerging technology of knowledge representation and information sharing today. Fuzzy logic is a very popular technique of artificial intelligence which deals with imprecision and uncertainty. The classical ontology can deal ideally with crisp data but cannot give sufficient support to handle the imprecise data or information. In this paper, we incorporate fuzzy logic with heavyweight ontology to solve the imprecise information extraction problem from heterogeneous misty sources. Fuzzy ontology consists of fuzzy rules, fuzzy classes and their properties with axioms. We use Fuzzy OWL plug-in of Protege to model the fuzzy ontology. A prototype is developed which is based on OWL-2 (Web Ontology Language-2), PAL (Protege Axiom Language), and fuzzy logic in order to examine the effectiveness of the proposed system.

• 산업기술연구
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• 제16권
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• pp.231-237
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• 1996

In this paper, the nonlinear I/O characteristic of fuzzy logic controller is analyzed by using cell concept. Sources of the nonlinearity in a fuzzy logic controller include the fuzzification, the fuzzy reasoning and the defuzzification. A closed form expression for the defuzzified output is derived in case of a fuzzy logic controller with two inputs, triangular memberships, MacVicar-Whelan type linguistic rules, and direct fuzzy reasoning. As a result, it is shown that fuzzy logic controller is a nonlinear controller. Also its nonlinearity is analyzed with respect to the conventional PID control and the sliding mode control.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2000년도 추계공동학술대회논문집
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• International Journal of Precision Engineering and Manufacturing
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• 제6권2호
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• pp.40-45
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• 2005

This paper has presented an application of an optimum grinding conditions based on the fuzzy logic. Fuzzy logic can handle vague and uncertain knowledge, and presents a scheme for integrating data with various kinds of grinding data. Especially, this research is capable of determining the grinding conditions taking into account some fuzzy membership function represented for trapezoidal form such as hardness and surface roughness of workpiece, material tensile strength and elongation, and requirement of grinding method. Larsen's fuzzy production method utilizing the fuzzy production rule can be applied on the establishment of grinding conditions, and also the output value obtained by the center of gravity method can effectively utilize the optimum grinding conditions.