| dc.contributor.advisor | 蔡炎龍 | zh_TW |
| dc.contributor.advisor | Tsai,Yen lung | en_US |
| dc.contributor.author (Authors) | 林祐宇 | zh_TW |
| dc.creator (作者) | 林祐宇 | zh_TW |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 11-Oct-2011 16:56:07 (UTC+8) | - |
| dc.date.available | 11-Oct-2011 16:56:07 (UTC+8) | - |
| dc.date.issued (上傳時間) | 11-Oct-2011 16:56:07 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0096751012 | en_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) | 96751012 | zh_TW |
| dc.description (描述) | 98 | zh_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.................................... iii1 緒論 12 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....................... 113 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 ...................... 244 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) . . . . . 275 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/#G0096751012 | en_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) | RBF | en_US |
| dc.subject (關鍵詞) | temporal RBF | en_US |
| dc.subject (關鍵詞) | ANN | en_US |
| dc.subject (關鍵詞) | time series | en_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 Networks | en_US |
| dc.type (資料類型) | thesis | en |
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