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RISS 인기검색어
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- 주제어 : Real-Coded Genetic Algorithm(실수 코딩 유전자 알고리즘) (검색결과 5 건)
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황재승,이충남 한국풍공학회 2009 한국풍공학회지 Vol.13 No.2
In this study, a procedure to estimated static wind loads on building structures using a real-code genetic algorithm is provided. In the process, the effect of the location and number of sensors on the accuracy of the estimation is profoundly investigated through numerical analysis. With actual static wind loads evaluated according to the KBC 2005 code, displacement responses of a thirty-story building are calculated and some of the displacement responses are used to identify the static wind loads. From the numerical results, it is shown that the accuracy of the estimation is sensitive to the performance index of the genetic algorithm and the accuracy can be enhanced when the location of sensors is uniformly distributed over the building. 본 연구에서는 실수코딩 유전자알고리즘을 이용하여 정적풍하중을 추정하는 과정에서 중요한 요소인 측정센서의 개수와 위치가 미치는 영향을 분석하는 것을 목적으로 하였다. 영향을 분석하는 방법은 수치적인 방법을 이용하며 실수코딩 유전자알고리즘을 이용한 해석후의 실제 풍하중 추정정도의 정확도에 따라 측정센서의 개수와 위치에 의한 영향을 평가하였다. 첫 단계로써 코드에 따른 정적풍하중을 생성하여 30층 구조물에 대한 각 층의 풍하중, 전단강성행렬, 정적변위를 구한 후 다음 단계로써 유전자알고리즘을 이용하여 센서의 개수와 위치에 따라 구조물의 응답을 사용하여 풍하중을 추정하였다. 유전자알고리즘의 목적함수는 실제풍하중과 또는 실제풍하중과 유사하게 생성한 풍하중을 이용하여 만들 수 있다. 본 연구에서는 센서의 개수와 위치에 따른 각 변수의 최단시간 도출에 대한 내용을 분석하는데 중점을 두어 실제풍하중을 목적함수로 이용하였다.
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한성현 한국공작기계학회 2004 한국생산제조학회지 Vol.13 No.3
This paper proposed trajectory tracking control based on genetic algorithm. Trajectory tracking control scheme are real coding genetic algorithm(ROGA) and back-propagational algorithm(BPA). Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studies have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using real coding genetic algorithm and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verify numerical simulations and the results show better performance than constant gain controller
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남윤주(Yun-Joo Nam) 유공압건설기계학회 2021 유공압건설기계학회 학술대회논문집 Vol.2021 No.11
This paper presents the performance optimization of a Down-The-Hole (DTH) percussive drilling hammer using GA (Genetic Algorithm) and PSO (particle swarm optimization). The objective function for the optimization is the impact transmitted to the rock representing the measure combining the percussive force with blow per minute (BPM). The design parameters to be optimized are related to the inlet and outlet opening area through which the pressurized air is passed from and to the air chambers of the hammer. The optimization method consists of two parts: one is real-coded GA featuring rank-based stochastic universal sampling, blend alpha crossover, non-uniform mutation and elitism, and the other is PSO featuring momentum and local/global attractions. Finally, the performance improvement via the optimization methods is presented and compared.
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GA 및 PSO 기법을 이용한 DTH 천공 햄머 성능 최적화
남윤주(Y. J. Nam) 유공압건설기계학회 2020 유공압건설기계학회 학술대회논문집 Vol.2020 No.12
This paper presents the performance optimization of a Down-The-Hole (DTH) percussive drilling hammer using GA (Genetic Algorithm) and PSO (particle swarm optimization). The objective function for the optimization is the impact transmitted to the rock representing the measure combining the percussive force with blow per minute (BPM). The design parameters to be optimized are related to the inlet and outlet opening area through which the pressurized air is passed from and to the air chambers of the hammer. The optimization method consists of two parts: one is real-coded GA featuring rank-based stochastic universal sampling, blend alpha crossover, non-uniform mutation and elitism, and the other is PSO featuring momentum and local/global attractions. Finally, the performance improvement via the optimization methods is presented and compared.
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