Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/51587


Title: 動態輻狀基底函數類神經網路建構之研究
Dynamic Implement Radial Basis Function Networks
Authors: 林祐宇
Contributors: 蔡炎龍
Tsai,Yen lung
林祐宇
Keywords: 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
Date: 2009
Issue Date: 2011-10-11 16:56:07 (UTC+8)
Abstract: 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.
Reference: [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.
Description: 碩士
國立政治大學
應用數學研究所
96751012
98
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0096751012
Data Type: thesis
Appears in Collections:[應用數學系] 學位論文

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