• 산업기술연구
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• 제17권
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• pp.323-330
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• 1997

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.

• 한국산림과학회지
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• 제85권2호
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• pp.172-179
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• 1996

본 연구는 산림자원(山林資源)의 다목적(多目的) 이용(利用) 문제에 합리적으로 접근하기 위하여 퍼지 다목표(多目標) 선형계획법(線型計劃法)을 삼림경영계획에 응용하고자 시도되었다. 산림이 가지고 있는 두 가지 기능 즉, 목재생산기능(木材生産機能)과 공익기능(公益機熊)이 지역별로 적절히 발휘되면서 조화를 이룰때 산림자원의 다목적 이용문제가 해결될 수 있다. 여기에서는 공익기능 중에서 휴양기능(休養機能)이 발휘되고 있는 자연휴양림(自然休養林)을 대상으로 목재생산기능과 휴양기능이 합목적적(合目的的)으로 조합을 이룰 수 있는 모델을 개발하였다. 우리나라에서 최초로 조성된 유명산 자연휴양림 지역을 대상으로 자료를 수집하여 퍼지 다목표(多目標) 선형계획(線型計劃)모델을 구성하였다. 그리고 이 모델의 계산을 통해 얻어진 결과가 기존의 휴양림 경영계획서상의 자료와 비교 검토되었다. 이때 유명산 자연휴양림 면적 829ha를 대상으로 퍼지 다목표(多目標) 선형계획(線型計劃)모델을 적용한 결과 4분기(分期) 동안 수확경신(收穫更新) 가능한 면적이 410.8ha로 나타났고 이 때의 수확재적은 $57,904m^3$ 였으며, 이 휴양림(休養林)을 찾는 방문객수는 약 860만명 정도로 추정되었다. 퍼지 목표(多目標) 선형계획(線型計劃)모델에 의해서 목재생산계획(木材生産計劃)과 휴양계획(休養計劃)이 동시에 최적화(最適化) 될때의 최대구성함수(最大構成函數)값 ${\lambda}$는 0.29로 나타났다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.204-206
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• 2007

In this study, we introduce the optimization method of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation, The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods. Particularly, in parameter identification, we use the UNDX operator which uses multiple parents and generate offsprings around the geographic center off mass of these parents.

• 대한인간공학회지
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• 제17권1호
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• pp.37-46
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• 1998

A human sensibility ergonomics design supporting system was applied to the product development for the customer's satisfaction based on ergonomics technology. The system is composed of three major subsystems such as customer's sensibility analysis, inference mechanism, and presentation technology. The main approaches of the system are to analyze customer's sensibilities and to translate them into product design elements. The purpose of this paper is to develop a design supporting system in which the relationship between customer's sensibility and product design elements is reasoned by a MADM(Multi-Attribute Decision Making) fuzzy model. In this model, three variables such as multiple correlation coefficients, partial correlation coefficients, and category scores were used in reasoning process. The weighted value of the words were also considered in fuzzy decision process. As a case study, the design supporting system with the MADM fuzzy model was applied to the personnel computer design.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.236-236
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• 2000

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.42.2-42
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• 2001

This paper deals with a fuzzy sliding mode observer for nonlinear systems. A nonlinear system is approximated by a multiple model Takagi Sugeno fuzzy system and then transformed into a canonical form for which a nonlinear observer is constructed. This study presents a type of fuzzy sliding mode observer that deals with matched and unmatched uncertainties in the plant dynamics very effectively. The proposed method was validated by the example of a inverted pendulum.

• Ocean Systems Engineering
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• 제8권2호
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• pp.201-221
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• 2018

In this paper a comprehensive study for the structural control of Jacket platform with Magneto-Rheological (MR) damper is presented. The control is implemented as a closed loop feedback of the applied voltage in the MR Damper using fuzzy logic. Nine cases of combinations with MR damper are presented to complete the work. The selection of the MR damper (RD 1005-3) is based on the operating parameters (i.e., the range of frequency and displacement). Bingham model is used to obtain the control forces. The damping co-efficient of the model is obtained using empirical relationship between the voltage in the MR damper and input velocity from the structural members. The force acting on the structure is obtained from Morison equation using P-M spectrum. The results show that the reliable control was obtained when there was a continuous connection of multiple MR dampers with the lower levels of the structure. Independent MR dampers at different levels provided control within a range, while the MR dampers placed at alternate positions gave very high control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2630-2632
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• 2000

In this paper, a control technique of Takagi-Sugeno (TS) fuzzy systems with parametric uncertainties is developed. The uncertain TS fuzzy system is represented as an uncertain multiple linear system. The control problem of TS fuzzy system is converted into the stabilization problem of a uncertain multiple linear system. A sufficient condition for robust stabilization is obtained in terms of linear matrix inequalities (LMI). A Design example is illustrated to show the effectiveness of the proposed method.

• Smart Structures and Systems
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• 제16권6호
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• 한국지능시스템학회논문지
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• 제19권1호
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• pp.25-33
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• 2009

최근 시계열 예측에 결론부에 선형식을 갖는 TS 퍼지 모델이 많이 이용되고 있는데, 이의 예측 성능은 정상성과 같은 데이터의 특성과 밀접한 관련이 있다. 그러므로 본 논문에서는 특히 비정상 시계열 예측에 매우 효과적인 새로운 예측 기법을 제안하였다. 시계열의 패턴이나 규칙성을 잘 끌어내기 위한 데이터 전처리 과정을 도입하고 다중 모델 TS 퍼지 예측기를 구성한 뒤, 러프집합을 이용한 적응 모델 선택 기법에 의해 입력 데이터의 특성에 따라 가변적으로 적합한 예측 모델을 선택하여 시계열 예측이 수행되도록 하였다. 마지막으로 예측 오차를 감소시키기 위하여 오차 보정 메커니즘을 추가함으로써 예측 성능을 더욱 향상시켰다. 시뮬레이션을 통해 제안된 기법의 성능을 검증하였다. 제안된 기법은 예측 모델 구현과 예측 수행 과정에서 시계열 데이터의 특성들을 잘 반영할 수 있으므로 불확실성과 비정상성을 갖는 시계열의 예측에 매우 효과적으로 이용될 수 있을 것이다.