自適應機率神經網路

CHUR > College Computer Science and Informatics > Department of Information Management > Journal Articles > Item 987654321/31937

Please use this identifier to cite or link to this item: http://chur.chu.edu.tw/handle/987654321/31937

Title:	自適應機率神經網路
Authors:	葉怡成 Yeh, I-Cheng
Contributors:	資訊管理學系 Information Management
Keywords:	類神經網路;機率神經網路;變數重要性;函數映射;分類 artificial;network;probabilistic;network;variable;mapping;classification
Date:	2008
Issue Date:	2014-06-27 01:39:17 (UTC+8)
Abstract:	本研究提出自適應機率神經網路(Adaptive Probabilistic Neural Networks, APNN)，它包含三種參數：代表變數重要性的變數權值、代表樣本有效範圍的核寬倒數、及代表樣本可靠程度的資料權值。本研究提出自適應調整這些參數的演算法，藉由學習過程優化這些參數，使模型的準確度最佳化。為證明此網路的性能，本研究以三個人為的函數映射問題以及一個實際的分類問題來做測試，並與倒傳遞網路及機率神經網路做比較。結果證明自適應機率神經網路的模型準確度只略低於BPN，而遠優於PNN，且APPN的變 This study proposes adaptive probabilistic neural networks (APNN), which include three kinds of parameters: the variable weights representing the importance of input variables, the core-width-reciprocal representing the effective range of patterns, and th
Appears in Collections:	[Department of Information Management] Journal Articles

Files in This Item:

File	Description	Size	Format
p_m321_0005.pdf		33Kb	Adobe PDF	30	View/Open