• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.80-83
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• 2003

A Genetic algorithm is a searching algorithm that based on the law of the survival of the fittest. Multi-population Genetic Algorithms are a modified form of genetic algorithm. Therefore, experience with fuzzy logic and genetic algorithm has proven to be that a combination of them can efficiently make up for their own deficiency. The Multi-population Genetic Algorithms independently evolve subpopulations. In this paper, we suggest a new coding method that independently evolves subpopulations using the fuzzy logic controller. The fuzzy logic controller has applied two fuzzy logic controllers that are implemented to adaptively adjust the crossover rate and mutation rate during the optimization process. The migration scheme in the multi-population genetic algorithms using fuzzy logic controllers is tested for a function optimization problem, and compared with other group migration schemes, therefore the groups migration scheme is then performed. The results demonstrate that the migration scheme in the multi-population genetic algorithms using fuzzy logic controller has a much better performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.512-515
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• 1997

In this paper the fuzzy-genetic controller for path-tracking of WMRs is proposed. Fuzzy controller is implemented to adaptive adjust the crossover rate and mutation rate, and genetic algorithm is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-genetic controller is effective.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.297-300
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• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

• Journal of information and communication convergence engineering
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• 제6권2호
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• pp.222-227
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• 2008

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.

• 제어로봇시스템학회논문지
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• 제9권1호
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• pp.57-62
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• 2003

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.

• Journal of Biosystems Engineering
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• 제20권1호
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• pp.87-94
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• 1995

When farmer loads and unloads farm products with a trailer, linked to a tractor, the tractor-trailer is backed up to the loading duck. However, travelling backward is not easy and takes a time for even skilled operators. Therefore, unmanned backing up is necessary to save the effort. A backward controller of tractor-trailer was simulated using fuzzy logic and genetic algorithms. Operators drive the tractor-trailer back and forth several times for backing up to the loading duck. As the operators did it, a backward controller was designed using fuzzy logic. And genetic algorithms was applied to improve the performance of the backward controller. With the strings coded with the fuzzy membership functions, genetic operations were carried out. After 30 generations, the best fitted fuzzy membership functions were found. Those membership functions were used in the fuzzy backward controller. The fuzzy controller combined with genetic algorithms showed the better results than the fuzzy controller did alone.

• 대한전기학회논문지
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• 제41권9호
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• pp.1095-1103
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• 1992

An approach to fuse the fuzzy inference system which is able to deal with imprecise and uncertain information and genetic algorithms which display the excellent robustness in complex optimization problems is presented in this paper. In order to combine genetic algorithms and fuzzy inference engine effectively the new reasoning method is suggested. The efficient identification method of fuzzy rules is proposed through the adjustment of search areas of genetic algorithms. The feasibilty of the proposed approach is evaluated through simulation.

• Journal of Mechanical Science and Technology
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• 제15권5호
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• pp.580-591
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• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• 대한전기학회논문지:전력기술부문A
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• 제48권1호
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• pp.36-43
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• 1999

This paper proposes a new hybrid genetic algorithm for auto-tuning fuzzy controllers improving the performance. In general, fuzzy controllers use pre-determined moderate membership functions, fuzzy rules, and scaling factors, by trial and error. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a hybrid genetic algorithm. The object of the proposed algorithm is to promote search efficiency by the hybrid optimization technique. The proposed hybrid genetic algorithm is based on both the standard genetic algorithm and a modified gradient method. If a maximum point is not be changed around an optimal value at the end of performance during given generation, the hybrid genetic algorithm searches for an optimal value using the the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algoritms. Simulation results verify the validity of the presented method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.60-64
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• 1997

Designing fuzzy controller, there are difficulties that we have to determine fuzzy rules and shapes of membership functions which are usually obtained by the amount of trial-and-error or experiences from the experts. In this paper, to overcome these defects, genetic algorithms which is probabilistic search method based on genetics and evolution theory are used to determine fuzzy rules and fuzzy membership functions. We design a series compensation fuzzy controller, then determine basic structures, input-output variables, fuzzy inference methods and defuzzification methods for fuzzy controllers. We develop genetic algorithms which may search more accurate optimal solutions. For evaluating the fuzzy controller performances through experiments upon an actual system, we design the fuzzy controllers for the speed control of a DC series motor with nonlinear characteristics and show good output responses to reference inputs.