• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.159-165
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• 2002

This paper proposes a GA and Gradient Descent Method-based method for choosing an appropriate set of fuzzy rules for classification problems. The aim of the proposed method is to fond a minimum set of fuzzy rules that can correctly classify all training patterns. The number of inference rules and the shapes of the membership functions in the antecedent part of the fuzzy rules are determined by the genetic algorithms. The real numbers in the consequent parts of the fuzzy rules are obtained through the use of the descent method. A fitness function is used to maximize the number of correctly classified patterns, and to minimize the number of fuzzy rules. A solution obtained by the genetic algorithm is a set of fuzzy rules, and its fitness is determined by the two objectives, in a combinatorial optimization problem. In order to demonstrate the effectiveness of the proposed method, computer simulation results are shown.

• Proceedings of the KIEE Conference
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• 1999.11b
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• pp.153-156
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• 1999

In this paper, we design a GA-fuzzy precompensated PID controller for the load frequency control of two-area interconnected power system. Here, a fuzzy precompensated PID controller is designed as a fuzzy logic-based precompensation approach for PID controller. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing PID controller. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor membership function and control rules.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.739-742
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• 1997

This paper proposes the structure and parameter optimization technique of fuzzy neural networks using genetic algorithm. Fuzzy neural network has advantages of both the fuzzy inference system and neural network. The determination of the optimal parameters and structure of the fuzzy neural networks, however, requires special efforts. To solve these problems, we propose a new learning method for optimization of fuzzy neural networks using genetic algorithm. It can optimize the structure and parameters of the entire fuzzy neural network globally. Numerical example is provided to show the advantages of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.58-69
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• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.6
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• pp.53-60
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• 2000

In this paper, we design a GA-fuzzy controller for position control and anti-swing at the destination point. A genetic algorithm is used to complement the demerits such as the difficulty of the component selection of the fuzzy controller, namely, scaling factors, membership functions and control rules. Lagrange equation is used to represent the motion equation of trolley and load in order to obtain mathematical modelling. Simulation results show that the proposed control technique is superior to a conventional optimal control in destination point moving and modification.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.519-521
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• 1998

Fuzzy inference systems have found many applications in recent years. The fuzzy inference system design procedure is related to an expert or a skilled human operator in many fields. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. The messy genetic algorithm is used to obtain structurally optimized fuzzy neural network models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the problem of a time series estimation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.113-116
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• 2001

This paper analyzes the evolutionary process of a fuzzy logic controller using evolutionary activity. An evolutionary algorithm is commonly used to find solutions for given problems. However, little has been done on the analysis of the evolutionary pathways to the optimal solutions. This paper uses a genetic algorithm to construct a fuzzy logic controller for a mobile robot and applies evolutionary activity to measure the adaptability quantitatively. Evolutionary activity can be defined as the rate at which useful genetic innovations are absorbed in the population. By measuring the evolutionary activities, we will show quantitatively that the optimal fuzzy logic controller is not from other genetic phenomena like chance or necessity, but from the adaptability to a given encironment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.313-316
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• 2008

에어컨 시스템은 압축기(Compressor), 응축기(Condenser), 증발기(Evaporator)와 확장밸브(Expansion Valve)로 구성되며, 에어컨 시스템에서 과열도와 저압(증발기의 압력)은 시스템의 효율 증대 및 성능 개선과 안정성에 대하여 결정적인 영향을 미친다. 따라서, 과열도와 저압을 조절하기 위해, 각각의 압축기내의 인버터 주파수와 확장밸브의 개도 제어가 중요하며 선형과 비선형 시스템 모두에 대하여 견실한 성능을 나타내고, 외란에 대하여 강인한 성능을 보이는 퍼지 제어기를 설계한다. 본 논문에서는 과열도와 저압을 제어하기 위하여, 3대의 확장밸브와 1대의 압축기를 가진 에어컨 시스템에 대하여 다중 퍼지 제어기를 설계한다. 또한, 각 제어 플랜트에 대하여 최적의 퍼지 제어기를 설계하기 위하여 3가지 최적화 알고리즘을 사용한다. 즉, 직렬 유전자 알고리즘(Serial Genetic Algorithm; SGA)과 병렬 유전자 알고리즘인 계층적 공정 경쟁 유전자 알고리즘(Hierarchical Fair Competition Genetic Algorithm; HFCGA), 그리고 Particle Swarm Optimization(PSO)을 사용하여 다중 퍼지 제어기를 최적화하고 시뮬레이션의 결과를 비교한다.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.1
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• pp.44-54
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• 2023

In this study, the second-order Nomoto's nonlinear expansion model was implemented as a Tagaki-Sugeno fuzzy model based on the heading angular velocity to design the automatic steering system of a ship considering nonlinear elements. A Tagaki-Sugeno fuzzy PID controller was designed using the applied fuzzy membership functions from the Tagaki-Sugeno fuzzy model. The linear models and fuzzy membership functions of each operating point of a given nonlinear expansion model were simultaneously tuned using a genetic algorithm. It was confirmed that the implemented Tagaki-Sugeno fuzzy model could accurately describe the given nonlinear expansion model through the Zig-Zag experiment. The optimal parameters of the sub-PID controller for each operating point of the Tagaki-Sugeno fuzzy model were searched using a genetic algorithm. The evaluation function for searching the optimal parameters considered the route extension due to course deviation and the resistance component of the ship by steering. By adding a penalty function to the evaluation function, the performance of the automatic steering system of the ship could be evaluated to track the set course without overshooting when changing the course. It was confirmed that the sub-PID controller for each operating point followed the set course to minimize the evaluation function without overshoot when changing the course. The outputs of the tuned sub-PID controllers were combined in a weighted average method using the membership functions of the Tagaki-Sugeno fuzzy model. The proposed Tagaki-Sugeno fuzzy PID controller was applied to the second-order Nomoto's nonlinear expansion model. As a result of examining the transient response characteristics for the set course change, it was confirmed that the set course tracking was satisfactorily performed.