• 한국산학기술학회논문지
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• 제12권8호
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• pp.3636-3643
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• 2011

연구실에서의 전기 사고를 방지하기 위하여 실시하는 전기 분야의 정기 점검에 대한 문제점을 파악하고 점검 항목에 대해 실제적으로 적용할 수 있는 퍼지 추론 시스템을 개발한다. 연구실의 전기 안전 환경을 중심으로 공통적으로 적용할 수 있는 점검 항목을 도출하고 연구실 전기 안전 관리에 부합한 연구실 정기 점검을 위한 전기 분야 표준 점검 리스트를 개발한다. 이러한 표준 점검 리스트를 이용하여 언어적 애매함을 포함할 수 있는 항목을 선정하고 이들 항목에 대해 소속 함수를 정의한다. 또한 안전 등급에 대해서도 소속 함수를 정의한다. 이러한 퍼지 변수들을 이용하여 If-Then 형식의 퍼지 규칙을 형성하고 퍼지 추론 엔진을 통해 퍼지 추론 시스템을 개발한다. 이를 통해 연구실의 전기 사고를 방지하기 위한 지능적이고 효율적인 점검 및 전기 안전을 지속적으로 관리함으로써 전기 사고를 미연에 방지할 수 있을 것이다.

• 전자공학회논문지C
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• 제36C권12호
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• pp.69-77
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• 1999

본 논문에서는 간편 간접추론방법을 이용한 퍼지 디지털 PID 제어기의 설계 방법을 제안하였다. 제안된 퍼지 제어기는 선형 디지털 PID 제어기에서 유도하였으며, 간편 간접추론을 이용한 퍼지화부, 제어규칙 베이스 및 퍼지화부의 설계방법을 설명하였다. 제안된 퍼지 제어기는 종래의 디지털 PID 제어기를 기초로 설계하였으므로 구조를 이해하기 쉽고, 퍼지입력에 의한 비선형 특성을 가지므로 선형 및 비선형 플랜트에 적응 능력을 가진다. 또한 각 입력변수 별로 간편 간접추론방법을 사용하여 추론하므로 고속 추론이 가능하고, 퍼지규칙의 수가 증가하여도 쉽게 적용 가능하다. 제안된 제어기의 성능을 D. Misir 등이 사용한 선형 및 비선형 플랜트에 모의 실험하여 효용성을 입증하였다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 춘계학술대회 논문집
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• pp.785-786
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• 2007

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
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• pp.142-144
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• 1993

In this study, a Self-learning Neural-Fuzzy Networks is presented, Because of the fuzzy controller property, the designing problems of fuzzy if-then rules, membership functions and inference methods are very complex task. Thus in this paper we proposed the Neural-Fuzzy Networks composed by Sugeno and Takagi's fuzzy inference method and learned by using temporal back propagation algorithm. The proposed method can refine automatically the fuzzy if-then rules without human expert's knowledges. The induction motor servo system is used to demonstrate the effectiveness of the proposed control scheme and the feasibility of the acquired fuzzy controller. All results are supported by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 전력전자학술대회 논문집
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• pp.662-665
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• 2001

As the fuzzy control is applied to servo system the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$-level set decomposition of fuzzy sets by quantize $\alpha$-cuts. This method can be easily implemented with analog hardware. The influence of quantization levels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of do servo system. It examined useful with experiment for dc servo system.

• Smart Structures and Systems
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• 제23권1호
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• Structural Engineering and Mechanics
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• 제61권2호
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• pp.283-293
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• 2017

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

• Smart Structures and Systems
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• 제13권1호
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• pp.81-98
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• 2014

This paper presents the elastic buckling of smart lightweight column structures integrated with a pair of surface piezoelectric layers using artificial intelligence. The finite element modeling of Smart lightweight columns is found using $ANSYS^{(R)}$ software. Then, the first buckling load of the structure is calculated using eigenvalue buckling analysis. To determine the accuracy of the present finite element analysis, a compression study is carried out with literature. Later, parametric studies for length variations, width, and thickness of the elastic core and of the piezoelectric outer layers are performed and the associated buckling load data sets for artificial intelligence are gathered. Finally, the application of soft computing-based methods including artificial neural network (ANN), fuzzy inference system (FIS), and adaptive neuro fuzzy inference system (ANFIS) were carried out. A comparative study is then made between the mentioned soft computing methods and the performance of the models is evaluated using statistic measurements. The comparison of the results reveal that, the ANFIS model with Gaussian membership function provides high accuracy on the prediction of the buckling load in smart lightweight columns, providing better predictions compared to other methods. However, the results obtained from the ANN model using the feed-forward algorithm are also accurate and reliable.