On the Effectiveness of Integrating Probabilistic Neural Networks with Influence Diagrams

CCUR > College of Natural Science and Engineering > College of Engineering > College of Engineering > Chinese Culture University Hwa Kang Journal of Engineering > Item 987654321/22223

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/22223

Title:	On the Effectiveness of Integrating Probabilistic Neural Networks with Influence Diagrams
Other Titles:	論整合神經網路和影響圖的效能
Authors:	曾敏烈
Contributors:	工學院
Keywords:	neural networks influence diagrams complexity
Date:	2002-06-01
Issue Date:	2012-05-09 14:53:22 (UTC+8)
Abstract:	眾所皆知，神經網路(Neural Networks)具有良好的學習能力、錯誤容忍力、以及大量的平行處理能力；但是，欠缺推論和解釋能力。本研究旨在結合影響圖(Influence Diagrams)的經濟、效率和彈性，爲神經網路提供較接近人類的推論和解釋能力。特別是，此整合網路在降低神經網路結構複雜性和推論上的複雜性(Complexity)，具有很好的效能。 Although neural networks indeed offer important new approaches to information processing, it is now a general trend to incorporate conventional computer methodologies for inferences and explanations in the hope of getting closer to human performance. In this study, we propose influence diagrams as a means of not only providing probabilistic inference but also reducing system complexity.
Relation:	華岡工程學報 16期 p.27 -53
Appears in Collections:	[College of Engineering] Chinese Culture University Hwa Kang Journal of Engineering

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	704	View/Open