• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권3호
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• pp.190-196
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• 2006

In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model has been constructed by using piecewise linear damping force model. In this paper, the fuzzy logic control based on heuristic knowledge is combined with the skyhook control. And it is simulated for a quarter car model. The acceleration of the sprung mass is included in the premise part of the fuzzy rules to reduce the vertical acceleration RMS value of the sprung mass. Then scaling factors and membership functions are tuned using genetic algorithm to obtain optimal performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2033-2034
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• 2006

In this paper, we applied genetic algorithm to current control for robust control and stability. In conclusion, we prove that current control of genetic algorithm can be high efficiency.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization 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 hybrid optimization process, two general optimization mechanisms are explored. Thestructural 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.

• 디지털융복합연구
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• 제13권3호
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• pp.155-165
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• 2015

기업부도예측은 재무 분야에 있어 중요한 연구주제 중 하나로 1960년대 이후부터 꾸준히 연구되어져 왔다. 국내의 경우, IMF 사태 이후 기업부도예측에 관한 중요성이 강조되고 있다. 이에 본 연구에서는 보다 정확한 기업부도예측을 위해 높은 예측력과 동시에 과적합화의 문제를 해결한다고 알려진 SVM(Support Vector Machine)을 기반으로 퍼지이론(fuzzy theory)을 활용해 입력변수를 확장하고, 유전자 알고리즘(GA, Genetic Algorithm)을 이용해 유사 혹은 유사최적의 입력변수집합과 파라미터를 탐색하는 새로운 융합모형을 제시한다. 제안모형의 유용성을 검증하기 위하여 H은행의 비외감 중공업 기업 데이터를 이용하여 실험을 수행하였으며, 비교모형으로는 로짓분석, 판별분석, 의사결정나무, 사례기반추론, 인공신경망, SVM을 선정하였다. 실험결과, 제안모형이 모든 비교모형들에 비해 우수한 예측력을 보이는 것으로 나타났다. 본 연구는 우수한 예측 성능을 가진 다기법 융합 모형을 새롭게 제안하여, 부도예측 분야에 학술적, 실무적으로 기여할 수 있을 것으로 기대된다.

• 전기학회논문지
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• 제59권5호
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• pp.972-980
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• 2010

In this study, we introduce the design methodology of an optimized type-2 fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. The type-2 fuzzy cascade controller scheme consists of the outer controller and the inner controller as two cascaded fuzzy controllers. In type-2 fuzzy logic controller(FLC) as the expanded type of type-1 fuzzy logic controller(FLC), we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. The control parameters(scaling factors) of each fuzzy controller using HFCGA which is a kind of parallel genetic algorithms(PGAs). HFCGA helps alleviate the premature convergence being generated in conventional genetic algorithms(GAs). We estimated controller characteristic parameters of optimized type-2 fuzzy cascade controller applied ball & beam system such as maximum overshoot, delay time, rise time, settling time and steady-state error. For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.

• 한국지능시스템학회논문지
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• 제19권3호
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• pp.329-336
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• 2009

본 논문은 회전형 역 진자 시스템(Rotary Inverted Pendulum System : RIPS)의 제어를 위한 Fuzzy cascade 제어구조를 제안하고 병렬유전자 알고리즘의 하나인 계층적 공정 경쟁 기반 유전자 알고리즘(Hierarchical Fair Competition-based Genetic Algorithms : HFCGA)을 이용한 최적화 방법을 제시한다. 회전형 역 진자 시스템은 Rotating arm의 회전을 통해 Pendulum의 각도를 제어하는 시스템으로써 제어 목적은 Rotating arm을 원하는 위치에 오게 하고 진자를 수직 위치의 불안정 평형 점에 위치하도록 하는 것이다. 본 논문에서는 회전형 역 진자 시스템의 제어를 위해 두개의 Fuzzy 제어기로 구성된 Fuzzy cascade 제어 구조를 설계하고, HFCGA를 이용하여 설계된 제어기의 파라미터를 최적화한다. 시뮬레이션 및 실험에서 SGA와 HFCGA의 성능비교를 통해 HFCGA의 우수성을 보이고, LQR 및 PD cascade 제어기와 제안된 Fuzzy cascade 제어기의 성능 비교를 통하여 제안된 방법의 우수성을 보인다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.340-342
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• 2004

In this paper, we introduce a new category of fuzzy inference systems baled 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. 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 polyminial 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(GAs) and the least square method. The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Journal of Advanced Marine Engineering and Technology
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• 제25권1호
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• pp.162-170
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• 2001

This paper presents a scheme for designing a fuzzy model-bsaed state observer for nonlinear system. For this scheme, a Tagaki-Sugeno type fuzzy model whose consequent part is of the state space form is obtained. In describes the locally linear input/output relationship of a system. The parameters of the fuzzy model are adjusted using a genetic algorithm. Then. fuzzy full-order and reduced-order state observers are designed based on the fuzzy model. A set of simulation works is carried out to demonstrate the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.123.2-123
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• 2001

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics, etc. To deal with this difficulty, the neuro-fuzzy system and the genetic-fuzzy system were used in determining the feeding rate of the coagulant. The fuzzy system is excellently robust in multi-variables and nonlinear problems. Therefore it uses basic algorithm, but it is difficult to construct of the fuzzy parameter such as the rule table and the membership function, Therefore we made the neuro-fuzzy system and the genetic-fuzzy system with the fusion of learning algorithms and compared the performance of the two fuzzy systems. To apply these algorithms, we made the rule table, membership function from the actual operation data of the water treatment plant. We determined optimized feeding rate of coagulant using the fuzzy operation, and also compared ...

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.509-511
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• 1998

Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership function and fuzzy rule base, which is traditionally achieved by tedious trial-and-error process. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm(GA). The controller design space is coded in base-7 strings chromosomes, where each bit gene matches the 7 discrete fuzzy value. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a do-servo motor control system. It was presented in discrete fuzzy linguistic value, and used a membership function with Gaussian curve. The performance of this control system is demonstrated higher than that of a conventional PID controller and fuzzy logic controller(FLC).