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研究生:邱奕捷
論文名稱:以遺傳規劃法模式估計高分子溶解度參數並優化模式複雜度
論文名稱(外文):Genetic Programming Based Models for Predicting Solubility Parameters of Polymers with Various Model Complexities
指導教授:陳文智陳文智引用關係
指導教授(外文):Wen-Chih Chen
口試委員:阮明利王國斌
口試委員(外文):Roan, Ming-LihG. B. Wang
口試日期:2022-06-24
學位類別:碩士
校院名稱:中國文化大學
系所名稱:化學工程與材料工程學系奈米材料碩士班
學門:工程學門
學類:化學工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:83
中文關鍵詞:溶解度參數
外文關鍵詞:Solubility
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本論文探討在在不同的模式複雜度之下對估計高分子溶解度參數準確度的影響。研究中使用了一組六個參數的描述因子,包括:氫鍵或靜電引力強度hb、是否屬於聚烯烴類alk 、內能Eint、負電荷分佈Qii、分子內的氫原子最大正電荷QH鍵和高分子重複單元內的氮原子數目nN來估計97個聚合物的溶解度參數(δ),並且分別以多元線性迴歸分析與遺傳規劃法來建造溶解度參數的估計模式。在本研究中透過調整族群代數、族群大小、模型複雜度、基因樹深度和基因組數量等參數來獲得效能更好的遺傳規劃法估計模式。
我們使用了遺傳規劃法(Genetic Programming, GP)來預測溶解度參數,並且使用了GPTIPS[1]為軟體應用平台,它可用於符號式的資料探勘。GPTIPS所建立的模式是一多基因符號式的非線性迴歸方程式。經過訓練組的訓練與驗證組的驗證測試,為了能讓模型達到穠纖合度,因為有共線性的問題,所以使用逐步迴歸法挑選共線度,並分別使用六個變數並分成四組,接著縮小每組變數並觀察準確率以及模式複雜度。第一組所得到的均方根誤差訓練組1.156(R2=0.8816)與驗證組0.3232(R2=0.9643),第二組所得到的均方根誤差訓練組1.2198(R2=0.8696)與驗證組0.4376(R2=0.9384) ,第三組所得到的均方根誤差訓練組1.4913(R2=0.8060)與驗證組0.6358(R2=0.8636),第四組參所得到的均方根誤差訓練組1.5451(R2=0.7930)與驗證組0.7971( R2=0.7828)的結果。從測試的結果顯示與其他現存的估計模式比較,以遺傳規劃法估計高分子的溶解度參數可以得到非常準確的估計值並可優化模式的複雜度。經與其它相關文獻比較,本論文所提的遺傳規劃法估計高分子溶解度參數的表現優異[13],並且具有人類瞭解的符號結構表示與簡單又容易使用的優點。

In this article, a set of six-parameter descriptors, including hydrogen bond or electrostatic attraction strength hb, whether it belongs to polyolefin alk, internal energy Eint, negative charge distribution Qii, maximum positive charge of hydrogen atom in the molecule bond QH and the number of nitrogen atoms in the polymer repeating unit nN, were used to correlate with solubility parameters for polymers. Multiple linear regression analysis and genetic programming were used to generate the models. The genetic programming was implemented under the software application platform GPTIPS[1], which can be used for symbolic data mining. To reduce the redundancy of the models, the six-parameter descriptors were reselected into four sets according to their contribution for the regression. The parameters of GPTIPS, such as population algebra, population size, model complexity, depth of gene tree, and number of genomes, were adjusted to obtain a more efficient genetic programming estimation model while considering the model complexity.
The final optimum genetic programing-based models produced the training set root mean square errors(RMSEs) of 1.156(R2=0.8816, for set1), 1.2198(R2=0.8696, for set2), 1.4913(R2=0.8060, for set3) and 1.5451(R2=0.7930, for set4); and the validation set root mean square errors(RMSEs) of 0.3232(R2=0.9643, for set1), 0.4376(R2=0.9384, for set2), 0.6358(R2=0.8636, for set3) and 0.6358(R2=0.8636, for set4), respectively. It suggests that the models obtained here can predict the solubility parameters values of polymers and provide theoretical guidance for polymeric molecular designs. Haven the advantages of the symbolic models and they are easy to implement[13], the proposed models are accurate in the estimation of solubility parameter values for polymers.

目錄 V
圖目錄 VII
表目錄 IX
第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 研究方向 4
1-4 組織章節 5
第二章 遺傳規劃之架構與學習策略 6
2-1 引言 6
2-2 遺傳規劃法之基本結構 8
2-3 GPTIPS 14
2-4 簡單的認識GPTIPS控制參數 20
2-4-1 範例:用簡單範例示範遺傳規劃法 21
2-5 何謂是Overfitting(過度擬合)和underfitting(擬合不足) 27
2-5-1如何防止過度擬合 28
2-6結果與討論 31
第三章 溶解度參數之線性迴規模式 32
3-1 引言 32
3-2 線性回歸法 32
3-3 溶解度參數之描述因子 33
3-4 GUIDE(圖形使用者介面設計)之簡介 35
3-4-1資料來源類型 36
3-4-2數據處理 36
3-4-3數據篩選 36
3-4-4挑選驗證組 37
3-4-5模擬方法 38
3-4-6圖形意義 38
3-5 逐步回歸法 39
3-6 訓練組與驗證組之線性回歸模型 41
3-7 結果與討論 54
第四章 溶解度參數之遺傳規劃法 55
4-1引言 55
4-2 訓練方式 55
4-3 以遺傳規劃法建置之高分子溶解度參數 56
4-4 結果與討論 67
第五章 結論 69
符號說明 71
參考文獻 72

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