近年來由於計算科學、電腦軟硬體及網路的快速發展，在各個領域上利用電腦運算或網路蒐集到的資料量都大幅的增加，這些蒐集到的資料量龐大到傳統的資料處理方式無法處理，需要新型態的資料處理方式，所以資料探勘的發展十分迅速。
本研究主要目的為開發一資料探勘平台，使用R軟體完成使用彈性倒傳遞類神經網路演算法針對大量輸入資料建立多輸出目標之預測模型以及利用基因演算法求出所建立之預測模型的最佳參數組合解。本研究利用收集自參考文獻之130組資料，每組資料內含4個輸入參數與2個輸出目標，以本資料探勘平台建立相關之預測模型進行預測，結果顯示兩個輸出目標預測值之均方根相對誤差分別為2.69%以及4.22%，驗證了本資料探勘平台建立之預測模型之可靠度；本研究同時利用Rastrigin function驗證本資料探勘平台於搜尋最佳參數組合部分之可執行性與正確性。最後，本研究也對所開發之平台內使用之兩種演算法之重要參數變異對結果之影響做一討論。

In recent years, due to the rapid development of computational science, hardware and software of computer, and the internet, the amount of information collected in various fields using computer computing or internet has increased greatly. The amount of information collected to the traditional data processing can’t be handled. It needs a new type of data processing, so the development of data mining is very rapid.
The purpose of this study is to develop a set of data mining software, which is written by R language, such that the predicting model corresponding to a massive amount of input engineering data can be obtained by using artificial neural network algorithms and optimized the engineering problem by using genetic algorithms. In this study, 130 sets of data collected from an existed literature, each set of data contains four input parameters and two output targets, were input into the proposed data mining software to establish the relevant predicting model. The predicted results showed that the mean square root of relative error of the two outputs target is 2.69% and 4.22%, respectively, which verifies the reliability of the predicting model established by the proposed data mining software. Furthermore, this study also uses Rastrigin function to verify the feasibility and correctness of the proposed data mining software in searching for the optimal combination of input parameters. Finally, this study also discusses the variations of the important control parameters of the two algorithms used in the proposed data mining software on the impact of the results.

論文審定書 ................................ ................................ ................................ ..................... i
誌 謝................................ ................................ ................................ ............................ ii
摘 要................................ ................................ ................................ .......................... iii
Abstract ................................ ................................ ................................ ......................... iv
目錄 ................................ ................................ ................................ ................................ v
表目錄 ................................ ................................ ................................ ........................ viii
圖目錄 ................................ ................................ ................................ ............................ x
第一章 ................................ ................................ ................................ ............................ 1
1.1 研究背景 ................................ ................................ ................................ ....... 1
1.2 資料探勘簡介 ................................ ................................ ............................... 1
1.3 文獻回顧 ................................ ................................ ................................ ....... 3
1.3.1 類神經網路演算法進歷程 ................................ .............................. 3
1.3.2 類神經網路演算法於工程問題之應用 ................................ .............. 4
1.3.3 基因演算法進歷程 ................................ ................................ .......... 5
1.4 研究動機與目的 ................................ ................................ ........................... 6
1.5 全文架構 ................................ ................................ ................................ ....... 6
第二章 ................................ ................................ ................................ ............................ 8
2.1 類神經網路演算法原理簡介 ................................ ................................ ......... 8
2.1.1多層前饋式類神經網路 ................................ ................................ ..... 10
2.1.2倒傳遞類神經網路演算法 ................................ ................................ . 11
2.2 基因演算法原理簡介 ................................ ................................ ................... 13
2.2.1編碼 ................................ ................................ ................................ ..... 14
2.2.2初始母代族群 ................................ ................................ ..................... 14
2.2.3適應值 ................................ ................................ ................................ . 14
vi
2.2.4選擇與複製 ................................ ................................ ......................... 15
2.2.5交配 ................................ ................................ ................................ ..... 16
2.2.6突變 ................................ ................................ ................................ ..... 16
2.2.7菁英策略 ................................ ................................ ............................. 17
2.3 R語言軟體介紹 ................................ ................................ ............................ 17
第三章 ................................ ................................ ................................ .......................... 25
3.1 資料探勘平台整體建立與運作流程 ................................ ........................... 25
3.2 資料輸入 ................................ ................................ ................................ ....... 25
3.2.1資料輸入 ................................ ................................ ............................. 26
3.2.2資料預處理 ................................ ................................ ......................... 26
3.3 類神經網路預測模型建立 ................................ ................................ ........... 27
3.3.1 類神經網路演算法運作流程 ................................ ............................ 27
3.3.2 類神經網路演算法參數設定 ................................ ............................ 28
3.4 基因演算法找最佳參數組合 ................................ ................................ ....... 30
3.4.1 基因演算法運作流程 ................................ ................................ ........ 30
3.4.2 基因演算法參數設定 ................................ ................................ ........ 31
3.5平台運算結果的呈現 與輸出 ................................ ................................ ........ 33
3.5.1圖像化顯示 ................................ ................................ ......................... 33
3.5.2 輸出存檔格式 ................................ ................................ .................... 33
第四章 ................................ ................................ ................................ .......................... 45
4.1 驗證平台可靠度原始數據 ................................ ................................ ........... 45
4.2 驗證平台可靠度預測模型結果 ................................ ................................ ... 47
4.3 類神經網路演算法之參數對預測模型影響 ................................ ............... 49
4.3.1 隱藏層與神經元 ................................ ................................ ................ 49
4.3.2 閥值 ................................ ................................ ................................ .... 51
vii
4.3.3 初始輸入資料比例 ................................ ................................ ............ 52
4.4 驗證基因演算法之最佳參數組合結果 ................................ ....................... 53
4.5 基因演算法之參數對資料最佳組合影響 ................................ ........... 55
4.5.1族群個體數量 ................................ ................................ ..................... 55
4.5.2交配機率 與突變................................ ................................ ......... 56
4.5.3菁英制度個體數 ................................ ................................ ................. 58
4.5.4最大迭代次數 ................................ ................................ ..................... 59
第五章 ................................ ................................ ................................ .......................... 97
5.1 結論 ................................ ................................ ................................ ............... 97
5.2 未來展望 ................................ ................................ ................................ ....... 99
參考文獻 ................................ ................................ ................................ .................... 100
附錄 ................................ ................................ ................................ .................... 102

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