• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.1430-1434
• /
• 2003

In this paper, for the fuzzy-reaching law method which has the characteristic of elimination of chattering at sliding mode as well as the characteristic of fast response at the design of variable structure controller with reaching law, optimal solutions for the determination of parameters of fuzzy membership functions by using genetic algorithm are proposed. Generally, the design of fuzzy controller has difficulties in determining the parameters of fuzzy membership functions by using a tedious trial-and-error process. To overcome these difficulties, this paper develops genetic algorithm of an optimal searching method based on genetic operation, and to verify the validity of this proposed method it is simulated through 2 link robot manipulator.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.581-584
• /
• 1998

This paper presents a scheme for fuzzy modelling and control of continuous-time nonlinear systems using a genetic algorithm. A fuzzy model is characterized by fuzzy "if-then" rules whose consequence part has a linear dynamic equation as subsystem of the system. The parameters of the fuzzy model are adjusted by a genetic algorithm. Then a tracking controller which guarantees stability of the overall system is designed. The simulation result demonstrates the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.2
• /
• pp.131-137
• /
• 2001

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on only particular domian. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. This paper describes the application of random signal-based learning to a genetic algorithm in order to get well tuned fuzzy rules. The key of tis approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.1
• /
• pp.76-86
• /
• 2002

In this paper, we suggest a hybrid genetic algorithm reinforced by a fuzzy logic controller (flc-HGA) to overcome weaknesses of conventional genetic algorithms: the problem of parameter fine-tuning, the lack of local search ability, and the convergence speed in searching process. In the proposed flc-HGA, a fuzzy logic controller is used to adaptively regulate the fine-tuning structure of genetic algorithm (GA) parameters and a local search technique is applied to find a better solution in GA loop. In numerical examples, we apply the proposed algorithm to a simple test problem and two complex combinatorial optimization problems. Experiment results show that the proposed algorithm outperforms conventional GAs and heuristics.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.40.6-40
• /
• 2001

Fuzzy controls are widely used in industrial fields using experts knowledge base for its high degree of performance. Genetic Algorithm(GA) is one of the numerical method that has an advantage of optimization. In this paper, we present an acquisition method of fuzzy rules using genetic algorithm. Knowledge of the system is the key to generating the control rules. As these rules, a system can be more stable and it reaches the control goal the faster. To get the optimal fuzzy control rules and the membership functions, we use the GA instead of the experts knowledge base. Information of the system is coded the chromosome with suitable phenotype. Then, it is operated by genetic operator, and evaluated by evaluation function. Passing by the decoding process with the fittest chromosome, the genetic algorithm can tune the fuzzy rules and the membership functions automatically ...

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.7
• /
• pp.1314-1323
• /
• 2011

The maintenance of generating units is implicitly related with power system reliability and has a tremendous bearing on the operation of the power system. A technique using a fuzzy search method which is based on fuzzy multi-criteria function has been proposed for GMS (generator maintenance scheduling) in order to consider multi-objective function. In this study, a new technique using combined fuzzy set theory and genetic algorithm(GA) is proposed for generator maintenance scheduling. The genetic algorithm(GA) is expected to make up for that fuzzy search method might search the local solution. The effectiveness of the proposed approach is demonstrated by the simulation results on a practical size test systems.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.16 no.6
• /
• pp.730-735
• /
• 2006

This paper proposes a time series prediction method for the short term electrical load will) the fuzzy system and the genetic algorithm. At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction system may be obtained. We obtain good results for the time prediction of the short term electric load by the proposed algorithm. In addition we implement the graphic user interface for the proposed algorithms. Finally, we implement the regional prediction system for the electric load.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.140-140
• /
• 2000

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. In this paper, we use random process and simulated annealing instead of mutation operator in order to get well tuned fuzzy rules. The key of this approach is to adjust both the width and the center of membership functions so that the tuned rule-based fuzzy controller can generate the desired performance. The effectiveness of the proposed algorithm is verified by computer simulation.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.12
• /
• pp.985-992
• /
• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• Journal of Computational Design and Engineering
• /
• v.2 no.3
• /
• pp.137-147
• /
• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.