• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• 한국지진공학회논문집
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• 제4권4호
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• pp.83-91
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• 2000

합리적인 기기의 활률론적 지진위험도 평가를 위해서는 모델의 동특성에 대한 보다 현실적인 정보가 제공되어야 한다. 이 연구에서는 심한 비선형 동적 거동을 보일 것으로 예상되는 철제 전기 캐비넷의 동특성 시험결과 및 분석 절차를 제시하였다. 특히, 이 연구에서는 가진 강도의 크기에 따른 동특성의 비선형 집중분석하고, 그 비선형성의 원인을 고찰하였다. 시험 결과 및 이 논문에 제시된 분석 절차를 이용하여 시험체의 동특성이 효과적으로 도출될 수 있으며, 대상 시험체는 가진 강도에 따라 심한 비선형 거동을 함을 입증하였다. 비선형성의 원인은 일반적인 재료 비선형이라기 보다는 각 부품들의 마찰력과 기하학적인 비선형성에 기인함을 발견하였다. 또한, 캐비넷 형식의 기긱에 대한 합리적인 내진안전성 평가를 위해서는 각 방향별로 서로 다른 감쇠값을 적용할 것을 추천하였다. 또한, 캐비넷 형식의 기기에 대한 합리적인 내진안전성 평가를 위해서는 각 방향별로 서로 다른 감쇠값을 적용할 것을 추천하였다.

• Advances in robotics research
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• 제2권1호
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• pp.99-112
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• 2018

The main contribution of this work is the design of a field programmable gate array (FPGA) based ARX-Laguerre proportional-integral observation (PIO) system for fault detection and identification (FDI) in a multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. An ARX-Laguerre method was used in this study to dynamic modeling the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, fault detection, isolation, and estimation the proposed FPGA-based PI observer was applied to the ARX-Laguerre robot model. The effectiveness and accuracy of FPGA based ARX-Laguerre PIO was tested by first three degrees of the freedom PUMA robot manipulator, yielding 6.3%, 10.73%, and 4.23%, average performance improvement for three types of faults (e.g., actuator fault, sensor faults, and composite fault), respectively.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2001년도 춘계학술대회 논문집
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• pp.238-242
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• 2001

In this paper, we proposed an optimal identification method of the membership functions and the numbers of fuzzy rule base for the stabilization controller of the Thermal process control system by RVEGA. Although fuzzy logic controllers and expert systems have been successfully applied in many complex industrial process, they must rely on experts knowledges. So it is difficult in determination of the linguistic state space, definition of the membership functions of each linguistic term and the derivation of the control rules. To verify the validity of this RVEGA-based fuzzy controller, Thermal process control system, with strong nonlinear dynamics, was selected for application of this algorithm and compare with PI controller, and the empirically improved fuzzy controller.

• 한국생산제조학회지
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• 제21권1호
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• pp.76-84
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• 2012

This paper presents the measurement system of arrow's point of impact using laser scan camera and describes the image calibration method. The calibration process of distorted image is primarily divided into explicit and implicit method. Explicit method focuses on direct optical property using physical camera and its parameter adjustment functionality, while implicit method relies on a calibration plate which assumed relations between image pixels and target positions. To find the relations of image and target position in implicit method, we proposed the performance criteria based polynomial theorem model that overcome some limitations of conventional image calibration model such as over-fitting problem. The proposed method can be verified with 2D position of arrow that were taken by SICK Ranger-D50 laser scan camera.

• 한국음향학회지
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• 제15권4호
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• pp.77-82
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• 1996

본 연구에서는 샌드위치형 압전 트랜스듀서의 등가 회로 모델을 규명하는 방법을 제시하였다. 공기중에서 실험적으로 측정한 트랜스듀서의 전기적 어드미턴스와 이론적으로 계산된 어드미턴스의 오차가 최소가 되도록 하는 비선형 최적화 문제를 풀어 등가 회로에 관련된 미지 상수를 규명하였다. 예제 트랜스듀서에 대해 제안된 방법을 적용하여 등가회로를 모델링하고, 수중에서의 송신 음압 감도(TVR) 및 수신 읍압 감도(RVS)을 예측하고 실험치와 비교하여 규명된 등가 회로 모델의 타당성을 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.214-217
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• 1999

본 논문에서는 시그모이드 함수와 시그모이드 함수의 도함수로 유도한 RBF의 직교관계에 착안하여 은닉충에 직교함수를 활성화함수로 갖는 신경회로망을 제안한다. 제안하는 신경회로망을 직교신경회로망(ONN)이라고 한다. 제안한 방식의 유용성을 확인하기 위하여 비선형 함수의 근사 시뮬레이션에 의해 사상능력을 검토하고, 각각의 단일함수만을 적용한 경우와 비교·검토한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1009-1011
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• 1996

This paper presents an approach to building multi-input and single-output fuzzy models for nonlinear data-based systems. Such a model is composed of fuzzy rules, and its output is inferred by simplified reasoning. Optimal structure and membership parameters for a fuzzy model are automatically and simultaneously identified by GA(Genetic Algorithm). Numerical examples are provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce a fuzzy model with higher accuracy and a smaller number of fuzzy rules than the ones achieved previously in other methods.