• Proceedings of the KIEE Conference
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• 2007.04a
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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.

• The Transactions of the Korean Institute of Power Electronics
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• v.2 no.4
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• pp.45-55
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• 1997

According to the increase of factory-automation in the field of production, the importance of autonomous guided vehicle's(AGV) role is also increased. The study about an active and effective controller which can flexibly prepare for the changeable circumstance is in progressed. For this study, the research about action base system to evolve by itself is also being actively considered. In this paper, we composed an active and effective AGV fuzzy controller to be able to do self-organization. For composing it, we tuned suboptimally membership function using genetic algorithm(GA) and improved the control efficiency by the self-correction and generating the control rules. Self-organizing controlled(S0C) fuzzy controller proposed in the paper is capable of self-organizing by using the characteristics of fuzzy controller and genetic algorithm. It intuitionally controls AGV and easily adapts to the circumstance.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.296-300
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• 2012

최근 지구 온난화에 따른 이상 기후변화로 인해 게릴라성 집중호우와 같은 다양한 강우패턴이 발생되고 있다. 특히 집중호우의 빈도 및 규모가 커지고 있으며 피해 또한 증가하고 있다. 이에 대한 대안으로 하도의 정비, 댐 건설, 제방의 증고와 같은 구조적인 대책과 홍수예경보, 홍수보험, 통합홍수관리와 같은 비구조적인 대책에 대한 접근이 이루어지고 있다. 그러나 미래 기후변화에 대한 예측의 한계와 구조적 대책의 물리적 한계를 감안할 때 구조적 대책에 의한 방법만으로 변화하는 기후에 대응하여 홍수재해를 완벽하게 대처하기에는 부족한 것이 사실이다. 따라서 비구조적 대책에 의한 홍수피해저감이 절실히 필요하다. 따라서 본 연구에서는 국제수문개발계획 대표유역인 낙동강유역에 위치한 위천유역을 연구대상으로 선택하였고 이러한 중소규모의 유역에서 홍수예경보의 한계를 극복하고 신뢰성을 높이기 위하여 홍수유출시에 일어나는 유역내의 복잡한 물리적인 현상을 직접 고려하지 않고 입력자료와 출력자료의 관계로부터 학습과 추론을 통해 결론을 도출해내는 신경망, 퍼지, 유전자 알고리즘과 같은 Date Mining 기법을 사용하여 자동우량과 자동수위에 의한 하류 홍수예경보시스템을 구축하기 위해 수위를 예측하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.212-219
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• 2006

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.533-538
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• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.237-240
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• 2001

An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.221-226
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• 2006

In this paper, we study an approach to design a Fuzzy PI controller in HVDC(High Voltage Direct Current) system. In the rectifier of traditional HVDC system, turning on, turning off, triggering and protections of thyristors have lots of problems that can make the dynamic instability and cannot damp the dynamic disturbance efficiently. In order to solve the above problems, we adapt Fuzzy PI controller for the fire angle control of rectifier. The performance of the Fuzzy PI controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing(Genetic Algorithms, GAs). Then we can obtain factors of the Fuzzy PI controller by Genetic Algorithms. A comparative study has been performed between Fuzzy PI controller and traditional PI controller, to prove the superiority of the proposed scheme.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.588-590
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• 1999

In this paper the design method of a fuzzy logic controller with a genetic algorithm is proposed. Fuzzy logic controller is based on linguistic descriptions(in the form of fuzzy IF-THEN rules) from human experts. The auto-tuning method is presented to automatically improve the output performance of controller utilizing the genetic algorithm. The GA algorithm estimates automatically the optimal values of scaling factors and membership function parameters of fuzzy control rules. Controllers are applied to the processes with time-delay and the DC servo motor. Computer simulations are conducted at the step input and the output performances are evaluated in the ITAE.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.9-11
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• 2005

We propose a optimal identification of information granulation(IG)-based fuzzy model to carry out the model identification of complex and nonlinear systems. To optimally identity we use genetic algorithm (GAs) sand Hard C-Means (HCM) clustering. An initial structure of fuzzy model is identified by determining the number of input, the selected input variables, the number of membership function, and the conclusion inference type by means of GAs. Granulation of information data 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(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• Journal of Korean Association for Spatial Structures
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• v.2 no.3 s.5
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• pp.61-70
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• 2002

The objective of this study is the development of a size and shape discrete optimum design algorithms, which is based on the genetic algorithms and the fuzzy theory. This algorithms can perform both size and shape optimum designs of plane and space trusses. The developed fuzzy shape-GAs (FS-GAs) was implemented in a computer program. For the optimum design, the objective function is the weight of structures and the constraints are limits on loads and serviceability. This study solves the problem by introducing the FS-GAs operators into the genetic.