預測揮發性有機化合物的分配係數和表面粗糙度藉 由智慧型基因演算法(IGA)

Prediction of Partition Coefficients for Volatile Organic Compounds and Surface Roughness by Intelligent Genetic Algorithm (IGA) Method

楊上緯(Shang-wei Yang)

指導教授 ： 何信璋

國立虎尾科技大學/工程學院/自動化工程研究所/碩士(2011年)

摘要

近年來建模及預測被廣泛應用在很多地方,如股票指數、季節海浪的推
算、馬達控制器應用物體追蹤…等。本文以IGA 及適應性類神經模糊推論
系統(FNN)作為基礎的方法來預測揮發性有機化合物的分配係數，對於藥
物化學和環境化學而言揮發性有機化合物的分配係數，可用於研究有機化
合物在生物體內及環境中的遷移行為，因此這是一個重要的課題。而目前
臨床所使用的藥物大多都是有機化合物，分配係數越大則藥物越能夠在生
物體内自由傳輸，顺利通過生物膜到病菌，才能起到治療疾病的作用，因
此分配系數的研究是製成藥品的重大關鍵。我們應用IGA 及FNN 來建模及
預測以提升其製藥成功率。由於在製造業對於表面粗糙度的重視，它是用
來評估端銑加工上工件品質的準則。例如在表面密封、滾珠軸承、齒輪、
凸輪或軸頸等應用場合，我們應用IGA 及FNN 來優化表面粗糙度的建模及
預測。
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，能夠更有效率的預測出誤差較小的表面粗糙度和有機化物的分配係
數，並且優於前人的文獻及ANFIS 方法所預測出來的結果。

關鍵字

有機化合物分配係數表面粗糙度智慧型基因演算法(IGA) ； 建模 ； 預測

並列摘要

In recent years, modeling and forecasting is widely used in many places,
such as stock index, the projected seasonal waves, motor controller application
object tracking ... and so on. In this paper, IGA and adaptive Neuro-Fuzzy
Inference System (FNN) method as a basis to predict the distribution
coefficients of volatile organic compounds for medicinal chemistry and
environmental chemistry of volatile organic compounds in terms of the
distribution coefficient can be used to study organic compounds in in vivo
migration behavior and the environment, so this is an important issue. The
current clinical drugs used mostly organic compounds, the partition coefficient
month is more of drug free transfer in vivo can be successfully passed through
the membrane to the bacteria, can play a role in treatment of disease, so research
is the system partition coefficient into a major key drugs.We used to IGA and
FNN modeling and forecasting in order to enhance its pharmaceutical success
rate. As in the manufacturing sector for the importance of surface roughness,
which is used to assess the quality of the workpiece on the side milling criteria.
For example, the surface seal, ball bearings, gears, cams or journal and other
applications, we apply the IGA to optimize the surface roughness and FNN
modeling and forecasting.
The results indicate that, to the structure of FNN with the IGA to search for
the best FNN model parameters can be more efficient to predict the error of the
smaller surface roughness and the distribution coefficient of organic compounds.

並列關鍵字

Partition Coefficients for Volatile Organic Compounds ； Roughness ； Intelligent Genetic Algorithm (IGA) ； Modeling ； Prediction

參考文獻

與預測, ” 虎尾科技大學自動化工程工程研究所, 2009.

efficients of Volatile Organic Compounds using Genetic Algorithm an

9, pp.157–164,2008.

Coefficients for Volatile Organic Compounds for Volatile Organic C

o-Blood Distribution of volatile organic compounds,” Ecotoxicology a