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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一薰(I-Hsuan Hong) | |
| dc.contributor.author | Tzu-Yao Hsu | en |
| dc.contributor.author | 徐子堯 | zh_TW |
| dc.date.accessioned | 2021-05-19T17:40:14Z | - |
| dc.date.available | 2024-08-20 | |
| dc.date.available | 2021-05-19T17:40:14Z | - |
| dc.date.copyright | 2019-08-20 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-12 | |
| dc.identifier.citation | Barry, D. A. (1990). Supercomputers and their use in modeling subsurface solute transport. Reviews of Geophysics, 28(3), 277-295.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7208 | - |
| dc.description.abstract | 為順應綠色能源興起的潮流，海流能發電也是其中的選項之一，但研究該發電模式需要大量的實驗數據來進行模擬測試，在電腦模擬效率不夠應付研究需求的情況下，本研究以對海底渦流機的電腦模擬系統，以類神經網路架設後設模型，希望能取代電腦模擬系統，並且進行離線最佳化的方式，以求得的最佳解之參數來近似現實世界的物理實驗。此一架構主要分為兩大部分，首先以近似電腦模擬系統為目標，以類神經網路來當作此研究的後設模型。接著利用粒子團與基因演算法，以近似物理實驗結果為目標找出最佳解。本研究最終訓練出的類神經網路模型和電腦模擬系統間的誤差，以平均絕對百分比誤差的衡量，取得了百分之五以下的誤差，且以粒子團演算法所最佳化出來的結果，與物理實驗達到誤差低於十四個百分點以下。此一離線最佳化流程在不損失精準度的情況下，達到了降低運算時間以及提升效率的目的。 | zh_TW |
| dc.description.abstract | Simulations are used to estimate array efficiencies and environmental impacts in the marine turbine design. However, simulations need a considerable computation effort. This paper utilizes the neural network to develop our surrogate model describing the response surface between the design parameters of the simulation and experimental quantities of interest. The metaheuristics has been applied to searching for an optimal parameter setting of the simulation so that the error between the simulation and field experiment is minimized. Our numerical study demonstrates a set of parameter setting, which returns 14% error between the simulation and field experiment. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:40:14Z (GMT). No. of bitstreams: 1 ntu-108-R06546029-1.pdf: 2669229 bytes, checksum: 487ff25c13c63f4d1616fea09e1e7560 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii 目錄 iii 圖目錄 iv 表目錄 v 第一章 緒論 1 第二章 離線最佳化 5 2.1 交叉驗證 6 2.2 後設模型 7 2.2.1 模型選擇 7 2.2.2 類神經網路 8 2.3最佳化演算法 12 2.3.1 定義問題 12 2.3.2 粒子團演算法 13 2.3.3 基因演算法 16 第三章 數值分析 18 3.1 模型參數設置與樣本分布 18 3.2 後設模型擬合誤差 22 3.3 預測誤差 23 3.4 最佳解擬合誤差 24 3.5 最終誤差 25 第四章 結論 29 參考文獻 30 | |
| dc.language.iso | zh-TW | |
| dc.title | 以類神經網路及萬用啟發式演算法離線最佳化模擬系統參數-海底渦輪機應用 | zh_TW |
| dc.title | Simulation parameters optimization based on Neural network and metaheuristics - An application of marine turbine | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 藍俊宏,蘇哲平,黃奎隆 | |
| dc.subject.keyword | 電腦模擬系統,類神經網路,後設模型,粒子團演算法,基因演算法,離線最佳化, | zh_TW |
| dc.subject.keyword | Simulation,Neural network,Surrogate model,Metaheuristics,Offline optimization, | en |
| dc.relation.page | 33 | |
| dc.identifier.doi | 10.6342/NTU201903336 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2019-08-13 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-08-20 | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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|---|---|---|---|
| ntu-108-1.pdf | 2.61 MB | Adobe PDF | 檢視/開啟 |
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