• 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 ...

• 한국강구조학회 논문집
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• 제18권2호
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• pp.147-160
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• 2006

본 논문에서는 개선소성힌지해석과 유전자 알고리듬을 이용한 평면 강골조 구조물의 퍼지최적설계 방법을 제시하였다. 개선소성힌지해석에서는 강골조 구조물의 기하학적 비선형성을 고려하기 위해 보-기둥 요소의 안정함수를 사용하였으며, 재료적 비선형을 고려하기 위해 잔류응력, 소성힌지, 그리고 기하학적 불완전성 등에 의한 점진적인 강성감소모델을 사용하였다. 유전자 알고리듬에서는 토너먼트 선택방법과 마이크로 유전자 알고리즘을 사용하였다. 목적함수로는 구조물의 총중량을 사용하였으며, 제약조건으로는 하중-저항능력, 사용성, 연성도, 그리고 시공성에 관한 기준을 고려하였다. 퍼지최적설계에서는 명확한 목적함수와 퍼지제약을 가지는 경우에 한하여 허용 오차는 제한값의 5%로 선택하고 비소속함수와 레벨컷 방법을 이용하여 0에서 1까지 0.2간격으로 나누어 최적화하였다. 여러 평면 강골조 구조물의 최적설계를 수행하여 일반GA최적설계와 퍼지GA최적설계의 최적값을 비교하였다.

• 산업경영시스템학회지
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• 제46권1호
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• pp.15-22
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• 2023

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

• 전기학회논문지
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• 제58권8호
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• pp.1615-1623
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• 2009

In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, we introduce Type-2 Fuzzy Neural Networks (T2FNNs) optimized by means of Particle Swarm Optimization(PSO). T2FNNs exploit Type-2 fuzzy sets which have a characteristic of robustness in the diverse area of intelligence systems. Considering the on-site situation where it is not easy to obtain voltage phases to be used for PRPDA (Phase Resolved Partial Discharge Analysis), the PD data sets measured in the laboratory were artificially changed into data sets with shifted voltage phases and added noise in order to test the proposed algorithm. Also, the results obtained by the proposed algorithm were compared with that of conventional Neural Networks(NNs) as well as the existing Radial Basis Function Neural Networks (RBFNNs). The T2FNNs proposed in this study were appeared to have better performance when compared to conventional NNs and RBFNNs.

• 산업경영시스템학회지
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• 제13권22호
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• pp.51-57
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• 1990

A new interactive multiobjective decision making technique, which is called the fuzzy sequential proxy optimization technique, has been proposed. This technique is the revised version the sequential proxy optimization technique that the decision-maker's marginal rates of substitution is interpreted as type of L-R fuzzy numbers. It used to the square of normalized scalar product as the doptimalilry condition. However, this technique ignores the imprecise nature of a decision-maker's judgement of marginal rates of substitution. Also, it have a shortcoming that can be only applied over three objective functions. In this paper, considering the imprecise nature of a decision-maker's judgement, we presents an interactive fuzzy decision-making method on the basis of the decision-maker's MRS presented through the use of five types of membership functions including non-linear functions. FORTRAN programs that run in conversational mode are developed to implement man-machine interactive procedure.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 가을 학술발표회 논문집
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• pp.243-250
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• 1998

In this paper, for the fuzzy constraints not only fuzziness of the constraints relation but also uncertainties of the response of the structures, allowable limits of the constraints and structural design variables, etc. are considered,. so that the fuzzy optimization of the structures can involve more wide scope of the problem and the fuzzy optimal problem is more generalized. In the decision making of the structural design scheme, every possible cases of the fuzzy variables, random variables and fuzzy-random variables, etc. for the uncertainties of the optimization problem are all considered, so the most general method of the decision making is presented. And a numerical example for the three bar truss is offered to demonstrate the reliability and execution possibility proposed method in this paper.

• International Journal of Control, Automation, and Systems
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• 제2권3호
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• pp.298-309
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• 2004

In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.277-278
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• 2007

본 논문은 HVAC(heating, ventilating, and air conditioning) 시스템에 대해 Particle Swarm Optimization(PSO) 알고리즘을 이용하여 최적화된 Multi-Fuzzy 제어기 설계를 제안한다. HVAC 시스템의 효율과 안정도에 결정적인 영향을 미치는 과열도와 저압(증발기의 압력)을 제어하기 위해, 3대의 Expansion Valve 와 1대의 Compressor 에서 동시에 제어하는 Multi-Fuzzy 제어기를 설계한다. 그리고 최적화 알고리즘 중 하나인 사회적인 행동양식을 기반한 PSO 알고리즘을 이용하여 설계된 Multi-Fuzzy 제어기를 최적화한다. 시뮬레이션의 결과 비교를 통해, 대표적인 최적화 알고리즘인 유전자 알고리즘을 사용한 최적화된 제어기와 제안한 PSO 알고리즘을 이용한 최적화된 제어기의 성능을 평가한다.

• Journal of Power Electronics
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• 제12권5호
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• pp.830-841
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• 2012

Active power filters (APF) can be employed for harmonic compensation in power systems. In this paper, a fuzzy based method is proposed for identification of probable APF nodes of a radial distribution system. The modified adaptive particle swarm optimization (MAPSO) technique is used for final selection of the APFs size. A combination of Fuzzy-MAPSO method is implemented to determine the optimal allocation and size of APFs. New fuzzy membership functions are formulated where the harmonic current membership is an exponential function of the nodal injecting harmonic current. Harmonic voltage membership has been formulated as a function of the node harmonic voltage. The product operator shows better performance than the AND operator because all harmonics are considered in computing membership function. For evaluating the proposed method, it has been applied to the 5-bus and 18-bus test systems, respectively, which the results appear satisfactorily. The proposed membership functions are new at the APF placement problem so that weighting factors can be changed proportional to objective function.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1158-1160
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• 1996

In this paper, the optimization of fuzzy controller using genetic algorithm is studied. The fuzzy controller has been widely applied to industries because it is highly flexible, robust, easy to implement, and suitable for complex systems. Generally, the design of fuzzy controller has difficulties in determining the structure of the rules and the membership functions. To solve these problems, the proposed method optimizes the structure of fuzzy roles and the parameters of membership functions simultaneously in so off-line method. The proposed method is evaluated through computer simulations.