| dc.contributor.advisor | 蔡瑞煌 | zh_TW |
| dc.contributor.advisor | Tsahi Ray | en_US |
| dc.contributor.author (Authors) | 柯文乾 | zh_TW |
| dc.contributor.author (Authors) | Ke, Wen-Chyan | en_US |
| dc.creator (作者) | 柯文乾 | zh_TW |
| dc.creator (作者) | Ke, Wen-Chyan | en_US |
| dc.date (日期) | 2002 | en_US |
| dc.date.accessioned | 18-Sep-2009 14:22:32 (UTC+8) | - |
| dc.date.available | 18-Sep-2009 14:22:32 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-Sep-2009 14:22:32 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0090356002 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/35180 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理研究所 | zh_TW |
| dc.description (描述) | 90356002 | zh_TW |
| dc.description (描述) | 91 | zh_TW |
| dc.description.abstract (摘要) | 神經網路已經被成功地應用於解決各種分類及函數近似的問題，尤其因為神經網路是個萬能的近似器(universal approximator)，所以對於函數近似的問題效果更為顯著。以往對於此類問題雖然多數以線性的分析工具為主，但是實際上多數問題本質上是非線性的，所以對於非線性分析工具的需求其實是很大的。自1986年起，神經網路本身的運作一直被視為一個黑箱作業，難以判斷網路學習結果的合理性，更無法有效地幫助使用者增進其知識，因此提供一套合理及有效的神經網路分析方法是重要。本文提出一套分析神網路系統的方法；利用線性規劃的技巧萃取及分析網路中的規則(rule)，而不需要對任何資料集做分析；進而利用統計無母數方法－符號檢定－歸納出網路中的知識。以債券評價為例，驗證此方法的可行性，實證結果亦顯示此方法所萃取出來的規則是合理的，且由這些萃取出的規則中，所歸納出來有關債券評價的知識多數是合理的。 | zh_TW |
| dc.description.abstract (摘要) | Neural networks have been successfully applied to solve a variety of application problems including classification and function approximation. They are especially useful for function approximation problems because they have been shown to be uni-versal approximators. In the past, for function approximation problems, they were mainly analyzed via tools of linear analyses. However, most of the function approxi-mation problems needed tools of nonlinear analyses in fact. Thus, there is the much demand for tools of nonlinear analyses. Since 1986, the neural network is considered a black box. It is hard to determine if the learning result of a neural network is rea-sonable, and the network can not effectively help users to develop the domain knowl-edge. Thus, it is important to supply a reasonable and effective analytic method of the neural network.Here, we propose an analytic method of the neural network. It can extract rules from the neural network and analyze them via the Linear Programming and does not depend on any data analysis. Then we can generalize domain knowledge from these rules via the sign test, a statistical non-parameter method. We take the bond-pricing as an instance to examine the feasibility of our proposed method. The result shows that these extracted rules are reasonable by our method and that these generalized domain knowledge from these rules is also reasonable. | en_US |
| dc.description.tableofcontents | Contents1. Introduction 12. Related Works 42.1 Cluster Analysis 42.1.1 Chi2 Algorithm (Liu and Setiono, 1995) 42.2 Rule Extraction Techniques 62.2.1 NeuroLinear (Setiono and Liu, 1997) 6Neural Network Training and Pruning 6Rule Generation 82.2.2 STARE (Zhou et al., 2000) 10Data Generation 10Continuous Attribute Processing 10Rule Creation 11Priority Formation 12Fidelity Evaluation 132.2.3 CREFANN (Gaweda et al., 2000) 15Rule Extraction Algorithm 15Rule-based Approximation Algorithm 162.2.4 REFANN (Setiono et al., 2002). 17Neural Network Training and Pruning Algorithm 17To Approximate Hidden Node Activation Function 20Rule Generation 222.2.5 RN2 (Satio and Nakano, 2002) 24Neural Network Training 24Method for Rule Extraction 262.3 Discussion 293. Methodology 323.1 Definition 323.2 Method of Extracting Rules from Neural Networks 333.2.1 The Approximation of Hidden Node Activation Function 333.2.2 The Differential Analysis of Rules 363.2.3 The Rule Extraction Process 384. Empirical Study 404.1 Bond Pricing 404.2 Data Collection and Method Application 424.3 Results and Analysis 495. Conclusions and Future Work 525.1 Conclusions 525.2 Future Work 53References 54 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0090356002 | en_US |
| dc.subject (關鍵詞) | 知識萃取 | zh_TW |
| dc.subject (關鍵詞) | 規則萃取 | zh_TW |
| dc.subject (關鍵詞) | 法則萃取 | zh_TW |
| dc.subject (關鍵詞) | 債券評價 | zh_TW |
| dc.subject (關鍵詞) | knowledge extraction | en_US |
| dc.subject (關鍵詞) | rule extraction | en_US |
| dc.subject (關鍵詞) | bond-pricing | en_US |
| dc.title (題名) | The Rule Extraction from Multi-layer Feed-forward Neural Networks | zh_TW |
| dc.type (資料類型) | thesis | en |
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