• 대한전기학회논문지:전력기술부문A
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• 제52권10호
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• pp.584-592
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• 2003

This paper presents a fuzzy-GA method for the allocation and operation of dispersed generator systems(DGs) based on load model in composite distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The problem formulation considers an objective to reduce power loss of distribution systems and the constraints such as the number or total capacity of DGs and the deviation of the bus voltage. The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm. The method proposed is applied to IEEE 12 bus and 33 bus test systems to demonstrate its effectiveness. .

• 융합신호처리학회논문지
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• 제10권1호
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• pp.93-99
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• 2009

퍼지제어, 신경망, 유전알고리즘은 시스템의 지능을 좀 더 높게 만들기 위한 알고리즘들이다. 본 논문은 원하는 응답을 얻기 위해 유전알고리즘을 사용해서 퍼지제어기를 최적화시켰다. 또한, 보상된 퍼지제어기는 두 개의 제어규칙을 갖는다. 하나의 제어규칙은 오버슛과 과도응답영역에서 일어나는 상승시간을 감소시키기 위해 사용하고 다른 하나는 정상상태오차를 줄이고 수렴영역에서의 수렴을 빠르게 가져가기 위해 사용된다. 유전알고리즘 제어기는 두 개의 퍼지 룰 베이스의 최적한 교체시기를 찾기 위해 사용하며 퍼지-유전알고리즘 제어기는 재생산, 교배와 변이의 과정을 갖는다. 그리고 유압서보 모터 제어시스템에 적용하여 제안한 알고리즘을 실험하였다. 실험 결과 보상된 FUZZY-GA제어기가 두 개의 룰 베이스를 갖는 퍼지제어 기술에 비해 좋은 제어성능을 가짐을 관찰하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.438-441
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• 1997

This paper studies a semi-active suspension with ER damper controlled Fuzzy Net Controller designed GA(Genetic Algorithm). Apparent viscosity of ERF(Electro-Rheological Fluid) can be changed rapidly by applying electric field. Semi-active suspension for ground vehicles are expected to improve ride quality with less vibration. This paper deals with a two-degree -of-freedom suspension using the ER damper for a quarter vehicle system. In this paper, the GA is applied for generating Fuzzy Net Controllers. The GA designs the optimal structure and performance of Fuzzy Net Controller having hybrid structure. Computer simulation results show that the semi-active suspension with ER damper has good performances of ride quality.

• 전기학회논문지P
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• 제51권2호
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• pp.99-104
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• 2002

In this paper, we construct a hybrid intelligent controller based on a fusion scheme of GA(Genetic Algorithm) and FCM(Fuzzy C-Means) clustering-based ANFIS(Adaptive Neuro-Fuzzy Inference System). In the structure identification, a set of fuzzy rules are generated for a given criterion by FCM clustering algorithm. In the parameter identification, premise parameters are optimally searched by adaptive GA. On the other hand, consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. Finally, we applied the proposed method to the truck backer-upper control and obtained a better performance than previous works.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.507-509
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• 1999

In this paper, we propose a new fuzzy model identification method that can yield a successful fuzzy rule base for fundamental approximations. The method in this paper uses a set of input-output data and is based on a hybrid messy genetic algorithm (mGA) with a fine-tuning scheme. The mGA processes variable-length strings, while standard GAs work with a fixed-length coding scheme. For successfully identifying a complex nonlinear system, we first use the mGA, which coarsely optimizes the structure and the parameters of the fuzzy inference system, and then the gradient descent method which tine tunes the identified fuzzy model. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a nonlinear approximation.

• 한국지능시스템학회논문지
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• 제13권2호
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• pp.209-215
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• 2003

A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

• 한국지능시스템학회논문지
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• 제13권1호
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• pp.107-113
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• 2003

A new GA-based methodology using information granules is suggested for the construction of fuzzy classifiers. The proposed scheme consists of three steps: selection of information granules, construction of the associated fuzzy sets, and tuning of the fuzzy rules. First, the genetic algorithm (GA) is applied to the development of the adequate information granules. The fuzzy sets are then constructed from the analysis of the developed information granules. An interpretable fuzzy classifier is designed by using the constructed fuzzy sets. Finally, the GA are utilized for tuning of the fuzzy rules, which can enhance the classification performance on the misclassified data (e.g., data with the strange pattern or on the boundaries of the classes). To show the effectiveness of the proposed method, an example, the classification of the Iris data, is provided.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.53-58
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• 2002

In the construction of successful fuzzy models for nonlinear systems, the identification of an optimal fuzzy model system is an important and difficult problem. Traditionally, sGA(simple genetic algorithm) has been used to identify structures and parameters of fuzzy model because it has the ability to search the optimal solution somewhat globally. But SGA optimization process may be the reason of the premature local convergence when the appearance of the superior individual at the population evolution. Therefore, in this paper we propose a new method that can yield a successful fuzzy model using VmGA(virus messy genetic algorithms). The proposed method not only can be the countermeasure of premature convergence through the local information changed in population, but also has more effective and adaptive structure with respect to using changeable length string. In order to demonstrate the superiority and generality of the fuzzy modeling using VmGA, we finally applied the proposed fuzzy modeling methodof a complex nonlinear system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.413-417
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• 2004

This paper proposes a fuzzy proportional-Integral (PI) controller for the precise tracking control of optical disk systems based on the genetic algorithm (GA). The fuzzy PI control rules are optimized by the GA to yield an optimal fuzzy PI controller. We validate the feasibility of the proposed method through a numerical simulation.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).