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研究生:黃忠雄
研究生(外文):Chung-Hsiung Huang
論文名稱:應用智慧型基因演算法(IGA)於造血幹細胞(HSCs)與粗糙度的建模與預測
論文名稱(外文):Intelligent Genetic Algorithm (IGA) for modeling and prediction of hematopoietic stem cells (HSCs) and roughness
指導教授:何信璋
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:機械與機電工程研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:47
中文關鍵詞:造血幹細胞(HSCs)粗糙度智慧型基因演算法(IGA)建模預測
外文關鍵詞:Hematopoietic stem cells(HSCs)RoughnessIntelligent Genetic Algorithm (IGA)ModelingPrediction
相關次數:
  • 被引用被引用:5
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
近年來基因演算法已經廣泛應用於工程等方面,包含自動控制、系統優化設計等。本文智慧型基因演算法(IGA)配合模糊神經網路(FNN)是用來預測工件端銑加工的表面粗糙度以及造血幹細胞生成。IGA是GA配合直交實驗設計的智慧型演算法,可有效推理出近似最佳解。
由於在製造業對於表面粗糙度相當重視,它是用來評估工件端銑加工的品質準則。例如在表面密封、滾珠軸承、凸輪、齒輪或軸頸等應用方面,表面粗糙度對於設備的性能影響很大。而巨核細胞(Mks)是一種極為罕見且非常重要的人體骨髓細胞,必須經過很複雜的演進過程才能生成造血幹細胞。
本文於粗糙度建模引用前人文獻中的48組訓練數據、24組驗證數據,使用FNN架構建模,總共有126個模型參數。由實驗結果得知,以FNN的架構配合IGA來搜尋最好的FNN模型參數,能夠準確的預測出誤差較小的表面粗糙度,並且優於前人的文獻及MATLAB的ANFIS 方法所預測出來的結果。
而對於造血幹細胞的生成預測,我們依舊採用FNN架構配合IGA的方法來建模與預測。以FNN的架構配合IGA來搜尋最好的FNN模型參數,能夠準確的預測出誤差較小的造血幹細胞,並且優於前人的文獻及MATLAB的ANFIS 方法所預測出來的結果。
Genetic algorithm used in engineering widely, including automatic control, system optimization design in recent years. Intelligent Genetic Algorithm(IGA) with Fuzzy Neural Network(FNN) is used to model and predict the workpiece surface roughness for the end milling process. IGA is powerful by using the Orthogonal Experimental Design’s algorithm. It can effectively reason to near-optimal solutions.
Surface roughness is very important in the manufacturing industry. It is used to assess the workpiece in the end milling process of performance criterion. For example:surface seals, ball bearing, cam, gear and journal. Surface roughness has very great impact for the equipment. Megakaryocytes (Mks) are an considerably rare cell population that are very important in myeloid cells, and produced by Hematopoietic stem cells(HSCs) through complex development processes.
In this paper, the model of the FNN uses previously researcher’s 48 training data and 24 validation data. There are 126 parameters to be optimized. Experimental results show that IGA with FNN model can improve the accuracy for modeling and prediction of surface roughness, and outperforms the ANFIS methods by MATLAB and reported recently in the literature.
In this paper, IGA with FNN is used to model and predict the HSCs production. Experimental results show that IGA with FNN model can improve the accuracy for modeling and prediction of HSCs, and outperforms the ANFIS methods by MATLAB and reported recently in the literature.
中文摘要.............................................................i
英文摘要............................................................ii
誌謝..................................................................iii
表目錄...............................................................vi
圖目錄..............................................................vii
符號說明..........................................................viii
第一章 導論........................................................1
1.1 前言.............................................................1
1.2 研究目的......................................................2
第二章 建模方式及工具介紹.................................3
2.1 粗糙度建模架構............................................3
2.2 造血幹細胞建模架構 .....................................4
2.3 ANFIS簡介...................................................5
2.4 歸屬函數介紹...............................................6
2.5 直交實驗設計介紹.........................................6
2.5.1 直交表範例 ...............................................7
2.5.2 直交表應用於IGA的交配使用方式................9
2.6 一般型基因演算法(SGA)介紹.......................10
2.7 智慧型基因演算法(IGA)介紹.........................10
第三章 實驗步驟與方法.....................................12
3.1 應用一:粗糙度的建模與預測.......................12
3.1.1 實驗數據.................................................12
3.1.2 數據歸一化..............................................14
3.1.3 ANFIS於粗糙度之預測..............................16
3.1.4 IGA於粗糙度之預測 .................................17
3.1.5 粗糙度IGA加入C++混合編成.....................17
3.2 應用二:造血幹細胞的建模與預測 ...............18
3.2.1 實驗數據.................................................18
3.2.2 數據編碼化 .............................................19
3.2.3 ANFIS於造血幹細胞之預測.......................21
3.2.4 IGA於造血幹細胞之預測...........................22
3.2.5 造血幹細胞IGA加入C++混合編成..............22
第四章 預測結果與分析 ....................................24
4.1 粗糙度結果分析..........................................24
4.2 粗糙度預測結果 .........................................24
4.3 造血幹細胞結果分析...................................26
4.4 造血幹細胞預測結果...................................27
第五章 結論.....................................................28
參考文獻.........................................................29
附錄一 ANFIS之建模參數指令...........................31
附錄二 粗糙度的所有建模參數...........................31
附錄三 造血幹細胞的所有建模參數.....................31
附錄四 ANFIS預測造血幹細胞的各種輸入組合.....32
附錄五 粗糙度預測加入的C++程式碼..................38
附錄六 造血幹細胞預測加入的C++程式碼 ...........40
Extended Abstract ...........................................43
簡歷.................................................................47
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