資料探勘輔助類神經演算法發展表面粗糙度預測系統之研究

Data Mining Assisted Neural Network in Surface Roughness Prediction System

吳士昌(Shi-Chang Wu)

指導教授 ： 黃博滄

中原大學/電機資訊學院/工業與系統工程研究所/碩士(2011年)

https://doi.org/10.6840/cycu201100904

摘要

智慧型控制理論與相關研究被廣泛運用在各領域中，且在各領域都有令人激賞的表現，但隨者科技日新月異與資料量無限的膨脹，現今智慧型系統因離群值的影響導致目標難以精準化的問題因此產生，此時資料探勘理論導入，解決了此相關問題。
本研究以類神經演算法為主軸作為預測之理論背景，資料探勘導入在於輔助類神經預測系統將每筆資料分析並找出其中的關連性，當目標確定就能依造每筆資料的關係強度分辨出可靠與不可靠資料，本研究假設使用可靠的資料作為類神經預測系統之訓練組資料，經過類神經網路訓練與測試準確預測出理想的結果。
由於資料探勘擁有將資料分析成可靠資訊之特性，並利用其資訊建立關聯性，再用此關聯性找出趨勢，此方法具有將資料轉換成未來趨勢之能力，也因此使資料探勘活用於各種領域中，但是在建構資料探勘模型的過程中將會遇到一個主要的問題：每筆數據間的規則如何建立？這一點是資料探勘建置過程中一定會遇到的困難，以往解決的辦法多半為聘請專家，以專家的經驗作為分析依據，本研究使用資料探勘中的貝氏理論，解決了聘請專家困擾，利用貝氏理論分析資料並以實驗結果驗證其可靠性。最後將資料探勘與類神經演算法整合，發展出一套完善的智慧型預測系統。
為證明所提出之方法的可靠性與準確性，本研究將發展結合資料探勘與類神經演算法導入至表面粗糙度預測實例中，建置出表面粗糙度預測系統，並與過去的迴歸模糊預測系統做為基礎並比較預測的準確性，最後利用假設檢定比較此兩種智慧型預測系統之顯著差異性，以驗證預測系統之準確性。

關鍵字

類神經演算法 ； 表面粗糙度 ； 資料探勘

並列摘要

Intelligent control theory has been studied in modern research and widely applied in various fields. With the rapid technological advances, however, intelligent control system becomes more complex and is difficult for researchers to define accurately. As a result, data mining is proposed to solve problems in the relevant areas.
In this study, the neural algorithm as the main theory background, Data mining is assisted into neural prediction system, the data analysis and identify each one of the relationship, When the target can be determined by making the relationship between the intensity of each data to distinguish reliable and unreliable information, The study assumed the use of reliable information as neural prediction system for the training set of data, through the neural network training and testing to predict the desired results.
As data mining has reliable information about the data analysis into the characteristics, and use information to establish its relationships, then this relationships to identify trends, this method has the data into the ability of future trends, therefore the data mining activities for a variety of area, but data mining models in the construction process will be a major problem encountered：How the rules between each data set up? this is the data mining process will build the difficulties, most of the previous solution to employ experts, expert's experience as a basis for analysis, this study used data mining of Bayesian theory, hire experts to solve problems, analyze data using Bayesian theory and experimental results verify its reliability. finally, the data mining algorithms integrated with neural algorithms, develop a comprehensive system of intelligent prediction.
For the demonstrate that the proposed method of reliability and accuracy, in this study, combined with the development of data mining algorithms and neural projections into the instance to the surface roughness, Build a surface roughness prediction system, and the return of the past as a basis for fuzzy prediction system and compare the accuracy of the forecasts, Finally, the use of hypothesis testing comparing the two kinds of intelligent forecasting system of the significant differences, to verify the accuracy of forecasting system

並列關鍵字

Neural algorithm ； Surface Roughness ； Data mining

參考文獻

31. 呂朋樺，「結合迴歸模糊與田口方法發展表面粗糙度預測系統之研究」，中原大學工業與系統工程碩士論文，2010。

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