期刊文献+

一种回声状态神经网络分类挖掘教学演示模型

A teaching demonstration model of classification mining based on echo state neural network
下载PDF
导出
摘要 回声状态神经网络分类算法是一种重要的数据挖掘方法,是处理大数据分类的重要工具。但该算法原理比较抽象,从公式推导的角度很难让学生深刻理解,因此提出利用MATLAB数学软件实现回声状态神经网络分类算法,将其编译成.NET平台的COM组件,由Visual C#.NET集成开发环境下的应用程序调用编译后的MATLAB的COM组件,实现回声状态神经网络的分类模型。 Echo state neural network classification algorithm is an important data mining method, which is an important tool to deal with classification of Big data. But the principle of the algorithm is abstract and complex, and it is very difficult for the students to understand the essence of the algorithm from the angle of formula derivation. In this paper, echo state neural network classification algorithm is realized by MATLAB mathematics software, and is compiled into COM component on.NET platform.The compiled COM component is called by an application program, which is developed in Visual C#.NET integrated environment,to realize the echo state neural network classification model.
作者 王华秋
出处 《计算机时代》 2016年第2期82-85,共4页 Computer Era
基金 重庆市研究生教育教学改革研究项目"研究生<大数据挖掘>课程案例与演示系统研制"(yjg143090) 国家社会科学基金一般项目"数字图书馆智能图像检索系统研制"(14BTQ053)
关键词 回声状态神经网络 分类模型 MATLAB组件 WINDOWS应用程序 echo state neural network classification model MATLAB component Windows application program
  • 相关文献

参考文献6

二级参考文献37

  • 1张燕,马永杰,袁秋林.Visual C#与Matlab混合编程方法及其实现[J].西北师范大学学报(自然科学版),2008,44(6):35-37. 被引量:13
  • 2赵士伟,赵明波,陈平.基于COM的MATLAB与C#.NET混合编程的实现与应用[J].山东理工大学学报(自然科学版),2006,20(4):26-29. 被引量:25
  • 3Jaeger H. The "Echo State" Approach to Analysing and Training Recurrent Neural Networks[ R]. Bremen: German National Research Center for Information Technology,2001.
  • 4Jaeger H, Haas H. Harnessing nonlinearity: Predicting chaotic system and saving energy in wireless communication[ J ]. Science,2004,304(5667) :78 - 80.
  • 5Skowronski M D, Harris J G. Automatic speech recognition using a predictive echo state network classifier[J]. Neural Networks, 2007,20(3) :414 - 423.
  • 6Ding Hai-yan, Pei Wenjiang, He Zhen-ya. A multiple objective optimization based echo state network tree and application to inmJsion detection[ A]. Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology[ C ]. Piscataway: Institute of Electrical and Electronics Engineers Inc, 2005.443 - 446.
  • 7Alexandre L A, Embrechts M J, Linton J. Benchmarking reservoir computing on time independent classification tasks [ A ]. Proceedings of the International Joint Conference on Neural Networks[ C]. Piscataway: Institute of Electrical and Electronics Engineers Inc,2009.89- 93.
  • 8Nisbach F, Kaiser M. Developmental time windows for spatial growth generate multiple-cluster small-world networks[ J]. European Physical Journal B,2007,58(2):185- 191.
  • 9Jaeger H. Tutorial on training recurrent neural networks, covering BPPT,RTRL,EKF and the "echo state network" approach [R]. Bremen: German National Research Center for Information Technology, 2002.
  • 10Kaiser M, Hilgetag C C. Development of multi-cluster cortical networks by time windows for spatial growth [ J ]. Neurocomputing,2007,70(10 - 12) :1829 - 1832.

共引文献24

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部