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基于极速学习的粗糙RBF神经网络
  • ISSN号：1000-7180
  • 期刊名称：微电子学与计算机
  • 时间：2012.8.8
  • 页码：9-14
  • 分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]中国矿业大学计算机科学与技术学院,江苏徐州221008, [2]中国科学院计算技术研究所,北京100190
  • 相关基金：国家自然科学基金（41074003,60975039）;中国科学院智能信息处理重点实验室开放基金（IIP2010-1）
  • 相关项目：多元空间数据的模式分析方法研究及其在测量中的应用
作者： 马刚|丁世飞|史忠植|MA Gang1,2,DING Shi-fei1,2,SHI Zhong-zhi2(1 School|2 Institute of Computing Technology,Chinese Academ|
关键词： ELM, R-RBF, Moore-Penrose, 极小范数最小二乘解, ELM, R-RBF~ Moore-Penrose, norm least-squares solution
中文摘要：

提出了一种用于训练粗糙RBF神经网络（rough RBF neural networks,R-RBF）的极速学习机（extreme learning machine,ELM）方法,通过引入矩阵的Moore-Penrose逆,将传统的迭代学习方法转换为一种求线性方程的极小范数最小二乘解的方法.实验证明,在训练精度、训练时间上都能够达到非常优越的性能,其泛化精度能够提升50%以上.

英文摘要：

The paper proposes a method of training rough RBF neural networks（R-RBF） using the extreme learning machine（ELM）, which eonverts the traditional iterative training method to solve norm least-squares solution of general linear system by introducing Moore-Penrose inverse. Experiments show that it can reach a very superior performance in both time and aeeuraey when ELM trains the Rough RBF Neural Networks, which can improve the generalization accuracy more than 50% compared with the traditional thinking of adjusting parameters iterative[y.

同期刊论文项目
多元空间数据的模式分析方法研究及其在测量中的应用
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分布式计算环境下的并行数据挖掘算法与理论研究
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期刊信息
  • 《微电子学与计算机》
  • 中国科技核心期刊
  • 主管单位:中国航天科技集团公司
  • 主办单位:中国航天科技集团公司第九研究院第七七一研究所
  • 主编：李新龙
  • 地址：西安市雁塔区太白南路198号
  • 邮编：710065
  • 邮箱：mc771@163.com
  • 电话：029-82262687
  • 国际标准刊号：ISSN：1000-7180
  • 国内统一刊号：ISSN：61-1123/TN
  • 邮发代号:52-16
  • 获奖情况:
  • 航天优秀期刊,陕西省优秀期刊一等奖
  • 国内外数据库收录:
  • 荷兰文摘与引文数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:17909