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題名 動態輻狀基底函數類神經網路建構之研究
Dynamic Implement Radial Basis Function Networks
作者 林祐宇
貢獻者 蔡炎龍
Tsai,Yen lung
林祐宇
關鍵詞 輻狀基底函數
暫時性輻狀基底函數
類神經網路
時間序列
RBF
temporal RBF
ANN
time series
日期 2009
上傳時間 11-十月-2011 16:56:07 (UTC+8)
摘要 近年來輻狀基底函數類神經網路 (Radial Basis Function Networks , RBFN) 應用在時間序列相關問題上已有相當的成果。在這篇論文裡，我們嘗試建構一個電腦軟體工具，可以很容易造出 RBFN，應用在時間序列預測相關問題上。更進一步的說，我們的電腦工具可以輕易做出即時修正，完全符合使用者的需求。我們一開始先複習 RBFN 的基本架構， 並說明如何應用到時間序列的問題上。接著我們研究近年來相當受到重視的 T-RBF (Temporal RBF) 架構。最後，我們解釋如何使用 Adobe Flex 去建構我們所需要的電腦軟體工具。這個工具是跨平台的程式，並且不論是雲端計算或是單機應用皆很合適。
During recent years, applying Radial Basis Function Networks (RBFN) to
time series problems yields many important results. In this thesis, we
try to implement a cross-platform computer tool that can easily
construct a RBFN applied to time series forecasting problems. Moreover,
the RBFN created by this computer tool can do real-time modification
to fit specific needs. We first review the basic structures of RBFN
and explain how it can be applied to time series problems. Then, we
survey on so called temporal radial basis function (T-RBF) model,
which draws much attention these years. Finally, we explain how we
use Adobe Flex to create a computer tool as we mentioned in the
beginning. The computer application is cross-platform and is suitable
for both cloud computing and desktop applications.
參考文獻 [1] M. D. Buhmann. Radial basis functions. Acta Numerica, 2000.
[2] S. Chen, C.F.N. Cowan, and P.M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. Neural Networks, IEEE Transac- tions on, 2(2):302 –309, mar 1991.
[3] S.P. Day and M.R. Davenport. Continuous-time temporal back-propagation with adaptable time delays. Neural Networks, IEEE Transactions on, 4(2):348 –354, mar 1993.
[4] Mustapha Guezouri. A New Approach Using Temporal Radial Basis Function in Chronological Series, 2008.
[5] Simon Haykin. Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall, 2 edition, July 1998.
[6] Robert J. Howlett and Lakhmi C. Jain. Radial Basis Function Networks 1: Re- cent Developments in Theory and Applications. Physica-Verlag HD; 1 edition, April 27, 2001.
[7] Daw-Tung Lin, Judith E. Dayhoff, and Panos A. Ligomenides. A Learning Algorithm for Adaptive Time-Delays in a Temporal Neural Network. 1992.
[8] D.T. Lin. The Adaptive Time-Delay Neural Network: Characterization and Applications to Pattern Recognition, Prediction and Signal Processing. 1994.
[9] D.T. Lin and J.E. Dayhof. Network Unfolding Algorithm and Universal Spa- tiotemporal Function Approximation. Technical research report tr95-6, Insti- tute for system research ISR, University of Maryland, 1995.
[10] M. J. D. Powell. Radial basis functions for multivariable interpolation: a review. pages 143–167, 1987.
[11] N.K. Sinha and B. Kuszta. Modeling and identification of dynamic systems. Van Nostrand Reinhold, New York, 1983.
[12] C. Wohler and J.K. Anlauf. Real time object recognition on image se- quences with adaptable time delay neural network algorithm -application to autonomous vehicles. Image and Vision, 19(9–10):593–618, 2001.
[13] P. Yee and S. Haykin. A dynamic regularized radial basis function network for nonlinear, nonstationary time series prediction. Signal Processing, IEEE Transactions on, 47(9):2503 –2521, sep 1999.
[14] Paul V. Yee and Simon Haykin. Regularized radial basis function networks : theory and applications. Wiley-Interscience; 1 edition, April 2, 2001.
[15] 張斐章、張麗秋、黃浩倫. 類神經網路理論與實務. 東華書局, 2004.
[16] 張麗秋、林永堂、張斐章. Building Radial Basic Function Neural Network by Integrating OLS and SGA for Flood Forecasting. Journal of Taiwan Water Conservancy, 2005.
[17] 林永堂. A Study of Combined OLS with SGA to Construct RBF Neural Networks for Flood Forecasting. 2004.
[18] 陳冠廷. The Application of Artificial Neural Networks in a Case-Based Design Wind Load Expert System for Tall Buildings. 2008.
[19] 陳映中. An Rbf Neural Network Method for Image Progressive Transmission. 2000.
描述 碩士
國立政治大學
應用數學研究所
96751012
98
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0096751012
資料類型 thesis
dc.contributor.advisor 蔡炎龍zh_TW
dc.contributor.advisor Tsai,Yen lungen_US
dc.contributor.author (作者) 林祐宇zh_TW
dc.creator (作者) 林祐宇zh_TW
dc.date (日期) 2009en_US
dc.date.accessioned 11-十月-2011 16:56:07 (UTC+8)-
dc.date.available 11-十月-2011 16:56:07 (UTC+8)-
dc.date.issued (上傳時間) 11-十月-2011 16:56:07 (UTC+8)-
dc.identifier (其他 識別碼) G0096751012en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/51587-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學研究所zh_TW
dc.description (描述) 96751012zh_TW
dc.description (描述) 98zh_TW
dc.description.abstract (摘要) 近年來輻狀基底函數類神經網路 (Radial Basis Function Networks , RBFN) 應用在時間序列相關問題上已有相當的成果。在這篇論文裡，我們嘗試建構一個電腦軟體工具，可以很容易造出 RBFN，應用在時間序列預測相關問題上。更進一步的說，我們的電腦工具可以輕易做出即時修正，完全符合使用者的需求。我們一開始先複習 RBFN 的基本架構， 並說明如何應用到時間序列的問題上。接著我們研究近年來相當受到重視的 T-RBF (Temporal RBF) 架構。最後，我們解釋如何使用 Adobe Flex 去建構我們所需要的電腦軟體工具。這個工具是跨平台的程式，並且不論是雲端計算或是單機應用皆很合適。zh_TW
dc.description.abstract (摘要) During recent years, applying Radial Basis Function Networks (RBFN) to
time series problems yields many important results. In this thesis, we
try to implement a cross-platform computer tool that can easily
construct a RBFN applied to time series forecasting problems. Moreover,
the RBFN created by this computer tool can do real-time modification
to fit specific needs. We first review the basic structures of RBFN
and explain how it can be applied to time series problems. Then, we
survey on so called temporal radial basis function (T-RBF) model,
which draws much attention these years. Finally, we explain how we
use Adobe Flex to create a computer tool as we mentioned in the
beginning. The computer application is cross-platform and is suitable
for both cloud computing and desktop applications.		en_US
dc.description.tableofcontents 誌謝...................................... i
中文摘要.................................... ii
Abstract.................................... iii
1 緒論 1
2 理論基礎 3
2.1 時間序列模型的目的與涵義 ....................... 3
2.1.1 預測的方法............................ 3
2.1.2 時間序列概念 .......................... 5
2.2 類神經網路................................ 5
2.2.1 人工類神經網路簡介....................... 5
2.2.2 類神經網路分類 ......................... 7
2.2.3 如何以神經網路作預測...................... 11
2.2.4 時間稽延類神經網路....................... 11
3 輻狀基底函數類神經網路 15
3.1 前言 ................................... 15
3.2 建構RBF網路 ............................. 15
3.3 RBF中心點之選取 ........................... 19
3.3.1 隨機選取法............................ 19
3.3.2 垂直最小平方法 ......................... 19
3.3.3 OLS中心點選取法計算步驟................... 23
3.4 動態輻狀基底函數類神經網路 ...................... 24
4 T-RBF (Temporal RBF) 方法 25
4.1 T-RBF網路架構 ............................ 25
4.1.1 網路展開平行演算法(Network Unfolding Algorithm) . . . . . 27
5 RBF 圖形介面化工具 31
5.1 AdobeFlex介紹 ............................ 31
5.2 使用AdobeFlex建構RBF應用程式 ................. 33
5.3 RBFN圖形介面工具使用說明...................... 34
5.3.1 設定區畫面............................ 34
5.3.2 資訊區畫面............................ 36
5.3.3 動態學習區畫面 ......................... 37
5.3.4 預測區畫面............................ 39
5.4 操作實例................................. 40
5.5 預測實例................................. 44
5.6 結論 ................................... 47
附錄 ................................... 49		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0096751012en_US
dc.subject (關鍵詞) 輻狀基底函數zh_TW
dc.subject (關鍵詞) 暫時性輻狀基底函數zh_TW
dc.subject (關鍵詞) 類神經網路zh_TW
dc.subject (關鍵詞) 時間序列zh_TW
dc.subject (關鍵詞) RBFen_US
dc.subject (關鍵詞) temporal RBFen_US
dc.subject (關鍵詞) ANNen_US
dc.subject (關鍵詞) time seriesen_US
dc.title (題名) 動態輻狀基底函數類神經網路建構之研究zh_TW
dc.title (題名) Dynamic Implement Radial Basis Function Networksen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] M. D. Buhmann. Radial basis functions. Acta Numerica, 2000.zh_TW
dc.relation.reference (參考文獻) [2] S. Chen, C.F.N. Cowan, and P.M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. Neural Networks, IEEE Transac- tions on, 2(2):302 –309, mar 1991.zh_TW
dc.relation.reference (參考文獻) [3] S.P. Day and M.R. Davenport. Continuous-time temporal back-propagation with adaptable time delays. Neural Networks, IEEE Transactions on, 4(2):348 –354, mar 1993.zh_TW
dc.relation.reference (參考文獻) [4] Mustapha Guezouri. A New Approach Using Temporal Radial Basis Function in Chronological Series, 2008.zh_TW
dc.relation.reference (參考文獻) [5] Simon Haykin. Neural Networks: A Comprehensive Foundation (2nd Edition). Prentice Hall, 2 edition, July 1998.zh_TW
dc.relation.reference (參考文獻) [6] Robert J. Howlett and Lakhmi C. Jain. Radial Basis Function Networks 1: Re- cent Developments in Theory and Applications. Physica-Verlag HD; 1 edition, April 27, 2001.zh_TW
dc.relation.reference (參考文獻) [7] Daw-Tung Lin, Judith E. Dayhoff, and Panos A. Ligomenides. A Learning Algorithm for Adaptive Time-Delays in a Temporal Neural Network. 1992.zh_TW
dc.relation.reference (參考文獻) [8] D.T. Lin. The Adaptive Time-Delay Neural Network: Characterization and Applications to Pattern Recognition, Prediction and Signal Processing. 1994.zh_TW
dc.relation.reference (參考文獻) [9] D.T. Lin and J.E. Dayhof. Network Unfolding Algorithm and Universal Spa- tiotemporal Function Approximation. Technical research report tr95-6, Insti- tute for system research ISR, University of Maryland, 1995.zh_TW
dc.relation.reference (參考文獻) [10] M. J. D. Powell. Radial basis functions for multivariable interpolation: a review. pages 143–167, 1987.zh_TW
dc.relation.reference (參考文獻) [11] N.K. Sinha and B. Kuszta. Modeling and identification of dynamic systems. Van Nostrand Reinhold, New York, 1983.zh_TW
dc.relation.reference (參考文獻) [12] C. Wohler and J.K. Anlauf. Real time object recognition on image se- quences with adaptable time delay neural network algorithm -application to autonomous vehicles. Image and Vision, 19(9–10):593–618, 2001.zh_TW
dc.relation.reference (參考文獻) [13] P. Yee and S. Haykin. A dynamic regularized radial basis function network for nonlinear, nonstationary time series prediction. Signal Processing, IEEE Transactions on, 47(9):2503 –2521, sep 1999.zh_TW
dc.relation.reference (參考文獻) [14] Paul V. Yee and Simon Haykin. Regularized radial basis function networks : theory and applications. Wiley-Interscience; 1 edition, April 2, 2001.zh_TW
dc.relation.reference (參考文獻) [15] 張斐章、張麗秋、黃浩倫. 類神經網路理論與實務. 東華書局, 2004.zh_TW
dc.relation.reference (參考文獻) [16] 張麗秋、林永堂、張斐章. Building Radial Basic Function Neural Network by Integrating OLS and SGA for Flood Forecasting. Journal of Taiwan Water Conservancy, 2005.zh_TW
dc.relation.reference (參考文獻) [17] 林永堂. A Study of Combined OLS with SGA to Construct RBF Neural Networks for Flood Forecasting. 2004.zh_TW
dc.relation.reference (參考文獻) [18] 陳冠廷. The Application of Artificial Neural Networks in a Case-Based Design Wind Load Expert System for Tall Buildings. 2008.zh_TW
dc.relation.reference (參考文獻) [19] 陳映中. An Rbf Neural Network Method for Image Progressive Transmission. 2000.zh_TW