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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
Keywords: 輻狀基底函數
temporal RBF
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.
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Description: 碩士
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Data Type: thesis
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