| dc.coverage.temporal | 計畫年度：90 起迄日期：20010801～20020731 | en_US |
| dc.creator (作者) | 陳春龍 | zh_TW |
| dc.date (日期) | 2001 | en_US |
| dc.date.accessioned | 18-Apr-2007 16:40:14 (UTC+8) | en_US |
| dc.date.accessioned | 8-Sep-2008 16:36:41 (UTC+8) | - |
| dc.date.available | 18-Apr-2007 16:40:14 (UTC+8) | en_US |
| dc.date.available | 8-Sep-2008 16:36:41 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-Apr-2007 16:40:14 (UTC+8) | en_US |
| dc.identifier (Other Identifiers) | 902416H004037.pdf | en_US |
| dc.identifier.uri (URI) | http://tair.lib.ntu.edu.tw:8000/123456789/3986 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/3986 | - |
| dc.description (描述) | 核定金額：554000元 | en_US |
| dc.description.abstract (摘要) | 迴歸分析與類神經網路是預測的兩種主要技術。過去幾年來已有超過50 篇以上的論文比較兩者在不同領域之應用及其預測效果。大部分的研究皆認為類神經網路的效果較佳，但是完整的比較則尚缺乏。 本論文嘗試在線性迴歸模式及非線性迴歸模式的條件下，隨機產生不同特性的資料以完整探討資料特性對迴歸分析與類神經網路之預測效果的影響。這些特性包括常態分配、偏態分配、非均等變異、 Michaelis-Menten 關係模式及指數迴歸模式。再者，我們使用區域搜尋法（Local Search ） 中的演化策略法(Evolution strategies，ES) 來當作類神經網路的學 習（Learning）方法以提高其預測功能。我們稱這種類型的類神經網路為ESNN。研究結果顯示資料特性確實會影響迴歸分析與類神經網路的預測效果。本研究歸納出一些規則以幫助使用者在面對資料時可以選擇適當的預測方法。此外，結果顯示ES 確實可提高類神經網路的預測效果。 | - |
| dc.description.abstract (摘要) | Regression analysis and artificial neural networks are two main techniques for prediction. During the past few years, more than fifty technical papers have been published for comparing the predictive performance of their applications in different areas. Most of these research concluded that artificial neural networks outperformed regression analysis; however, a more thorough comparison was lacking. In this research, we tried to randomly generate different types of data, so as to completely explore the effect of data characteristics on the predictive performance of regression analysis and artificial neural networks. The data characteristics include normal distribution, skew distribution, unequal variances, Michaelis-Menten relationship model and exponential regression model. In addition, we used the evolution strategies, one of the local search methods, to train artificial neural networks to further improve its performance. We named this type of artificial neural networks ESNN. Computational results indicate that data characteristics indeed affect the predictive performance of regression analysis and artificial neural networks. Several rules have been summarized to help user select suitable predictive methods for different types of data. The results also show that ES is able to enhance the predictive performance of artificial neural networks. | - |
| dc.format | applicaiton/pdf | en_US |
| dc.format.extent | bytes | en_US |
| dc.format.extent | 166102 bytes | en_US |
| dc.format.extent | 166102 bytes | - |
| dc.format.extent | 1465 bytes | - |
| dc.format.mimetype | application/pdf | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | text/plain | - |
| dc.language | zh-TW | en_US |
| dc.language.iso | zh-TW | en_US |
| dc.publisher (Publisher) | 臺北市：國立政治大學資訊管理學系 | en_US |
| dc.rights (Rights) | 行政院國家科學委員會 | en_US |
| dc.subject (關鍵詞) | 迴歸分析;類神經網路;區域搜尋法;演化策略法類神經網路 | - |
| dc.subject (關鍵詞) | Regression Analysis,Artificial Neural Networks,Local Search Methods,Evolution strategies neural network | - |
| dc.title (題名) | 資料特性對類神經網路預測效果影響之研究 | zh_TW |
| dc.title.alternative (其他題名) | A Study on the Effect of Data Characteristics on the Prediction Performance of Artificial Neural Networks | - |
| dc.type (資料類型) | report | en |
