 |
|
以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：26 、訪客IP：3.149.246.106
姓名 鄭又新(Yu-Hsin Cheng) 查詢紙本館藏 畢業系所 光機電工程研究所 論文名稱 仿螞蟻群聚最佳化神經網路於非線性迴歸之初步探討 相關論文 檔案 [Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
- 本電子論文使用權限為同意立即開放。
- 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
- 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中) 由類神經網路模式提出一種非線性迴歸的方法，該方法能有效地解決傳統多元迴歸分析難以解決的非線性多元迴歸問題。
本文中，嘗試利用混合蟻群最佳化和傳統倒傳遞學習演算法的方式建立一種新的神經網路學習演算法。使用這樣的學習規則，能正確地計算出神經網路適當的權值增加量。期望其可以讓神經網路學習的速度有顯著地提升，也能有效地改進其系統的準確度。而為了展示這樣學習技術的優點，設計幾個非線性系統問題做為試驗。並以原先的倒傳遞學習方式與其作為比較。從模擬結果可知此一學習技術較傳統倒傳遞學習方式效率佳。摘要(英) A nonlinear multiple regression analysis method based on artificial neural network is developed. The method can resolve nonlinear multiple regression problems effectively, when traditional multiple regression analysis may not capable of doing the job.
In this thesis, a new learning algorithm of neural network is developed by using ant colony optimization and back-propagation (BP) learning algorithm. Based on this learning algorithm, the appropriate weight increments of neural network can be computed and decided. Therefore, not only the learning of neural network can be greatly speeded, also the accuracy of neural network’’s performance can be effectively improved. For demonstrating the superiority of learning technique, several nonlinear system identification problems were tested. For comparison, same experiments were also performed by network with pure BP learning rule. From the experimental results, the learning technique we developed obviously has better performance as desired.關鍵字(中) ★ 非線性迴歸
★ 類神經網路
★ 蟻群最佳化關鍵字(英) ★ nonlinear regression
★ neural network
★ ant colony optimization論文目次 摘要.............................................................................................................I
Abstract......................................................................................................II
誌謝辭......................................................................................................III
目錄..........................................................................................................IV
圖目錄.....................................................................................................VII
表目錄......................................................................................................IX
第一章 緒論............................................................................................1
1.1 前言...............................................................................................1
1.2 研究動機與目的...........................................................................2
1.3 文獻回顧.......................................................................................4
1.3.1 類神經網路.............................................................................4
1.3.2 神經網路於迴歸分析應用.....................................................6
1.3.3 仿螞蟻群聚最佳化.................................................................8
1.4 研究限制.......................................................................................8
1.5 本文架構.......................................................................................9
第二章 研究背景..................................................................................10
2.1 類神經網路.................................................................................10
2.1.1 神經元模型............................................................................12
2.1.2 神經網路分類........................................................................15
2.1.3 神經網路發展........................................................................18
2.2 倒傳遞類神經網路.....................................................................20
2.2.1 倒傳遞網路之基本架構.......................................................21
2.2.2 最陡坡降法...........................................................................23
2.3 蟻群最佳化.................................................................................26
2.3.1 運作條件................................................................................27
2.3.2 運算模式................................................................................30
第三章 仿蟻群類神經網路..................................................................32
3.1 基本構想.....................................................................................32
3.1.1 探索........................................................................................33
3.1.2 開發........................................................................................34
3.2 運算方式.....................................................................................36
3.2.1 蟻群網路的學習與更新........................................................37
3.2.2 蟻群網路的狀態轉移............................................................39
第四章 實驗結果..................................................................................43
4.1 實驗方法.....................................................................................43
4.1.1 驗證方法................................................................................43
4.1.2 網路初值設定........................................................................44
4.2 實驗結果與探討.........................................................................45
4.2.1 實驗一....................................................................................45
4.2.2 實驗二....................................................................................48
4.2.3 實驗三....................................................................................51
第五章 結論與展望..............................................................................55
參考文獻..................................................................................................56
附錄一......................................................................................................59
附錄二......................................................................................................62
附錄三......................................................................................................66參考文獻 [1] 羅華強,“類神經網路-MATLab的應用”,高立圖書有限公司,2005.
[2] 施柏屹,“倒傳遞類神經網路學習收斂之初步探討”,中央大學機械工程研究所碩士論文,2000.
[3] 謝鎮鴻,“倒傳遞演算法則在旁路連結網路架構之收斂特性比較”,海洋大學機械與機電工程學系碩士論文,2006.
[4] 葉怡成,“類神經網路模式應用與實作”,儒林圖書有限公司,2003.
[5] 李志陞, “具有修正式學習法則之Sigma-Pi型網路研究”,義守大學電機工程學系碩士論文,2003.
[6] Amr Radi, and Riccardo Poli, “Genetic Programming Discovers Efficient Learning Rules for the Hidden and Output Layers of Feedforward Neural Networks.”, The University of Birmingham ,1998.
[7] Shynk, J.J. and Roy, S. “The LMS algorithm with momentum updating.”, IEEE International Symposium, on Circuits and Systems,1988.
[8] Lari-Najafi Hossein, Mohammed Nasiruddin, and Tariq Samad. “Effect of Initial Weight on Back-Propagation and Its Variations.”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 1, P218-219,1989.
[9] John K. Kruschke, and Javier R. Movellan, “Benefits of Gain: Speeded Learning and Minimal Hidden Layers in Back-Propagation Networks.”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 21, No.1, P273-280,1991.
[10] Genevieve B. Orr, Todd K. Leen, “Momentum and Optimal Stochastic Search.”, In Proceedings of the 1993 Connectionist Models Summer School, M.C. Mozer, P. Smolensky, D.S. Touretzky, J.L. Elman and A.s. Weigend (eds.), Erlbaum Associates, 1993.
[11] 劉育昌,“類神經網路快速學習之研究探討”,義守大學電機工程學系碩士論文,2006.
[12] 曾燕明,“類神經網路於配電系統負載預測及饋線開關操作之應用”,中山大學電機工程研究所博士論文,1995.
[13] 郭炳煌,“以統計方法與類神經網路模式預估工程直接成本之研究”, 高雄第一科技大學營建工程系碩士論文,2001.
[14] 梁晉嘉,“以非線性模式進行匯率走勢預測之研究－類神經網路模式之建立與應用”,中山大學經濟學研究所碩士論文,2001.
[15] 陳毓山,“最佳化倒傳遞類神經網路於土層剪力波速評估之研究”,台灣大學土木工程學研究所碩士論文,2005.
[16] 楊金聲,“利用類神經網路與線性迴歸進行成本預測之研究-以印刷電路板產業為例”,中原大學資訊管理學系碩士論文,2005.
[17] 盛嘉宏,“類神經網路與統計迴歸模式在暴潮之研究”,海洋大學海洋科學系碩士學位論文,2005.
[18] Dorigo, M., and Gambardella, L. M., “Ant Colonies for the Traveling Salesman Problem,” BioSystems, 43: pp. 73-81.1997.
[19] 蕭宗勝,“螞蟻族群演算法應用在組合問題之研究”,銘傳大學資訊管理研究所碩士論文,2002.
[20] 黃俊英,“多變量分析”,第七版,華泰書局,2000.指導教授 張江南(Chiang-Nan Chang) 審核日期 2007-7-1 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit