• Proceedings of the KAIS Fall Conference
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• 2003.11a
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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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• 1997.10a
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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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• v.6 no.2
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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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• Journal of Mechanical Science and Technology
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• v.15 no.5
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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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.964-967
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• 1996

In this paper, we present a fuzzy-sliding controller design using genetic algorithm. We can suppress chattering and enhance the robustness of controlled system by using this controller and do that genetic algorithm can easily find out a nearly optimal fuzzy rule performance of this controller is tested by simulation of car system with two pole.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.622-624
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• 2005

This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its 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 series system may be obtained. We obtain a good result for the time prediction of the electric load.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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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.