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粒度神经网络对数字语言的数据融合

Data Fusion of Numerical-Linguistic Based on Granular Neural Networks
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摘要 This paper presents a neural networks based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing,fuzzy computing, linguistic computing, and pattern recognition. A granular neural network (GNN)is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. The GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relation in a database. This paper presents a neural networks based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing,fuzzy computing, linguistic computing, and pattern recognition. A granular neural network (GNN)is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. The GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relation in a database.
作者 行小帅 焦李成
机构地区 西安电子科技大学雷达信号处理国家重点实验室
出处 《计算机科学》 CSCD 北大核心 2002年第11期40-43,共4页 Computer Science
基金 国家自然科学基金(60133010) 863高技术研究发展计划项目
关键词 粒度神经网络 数字语言 数据融合 知识发现 数据挖掘 数据库 人工智能 Granular neural networks,Data fusion,Data mining, Knowledge discovery
分类号 TP183 [自动化与计算机技术—控制理论与控制工程] TP311. [自动化与计算机技术—计算机软件与理论]
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参考文献9

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计算机科学
2002年 第11期

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