Cross validation model for neural network stability

doi:10.3778/j.issn.1002-8331.2010.34.013

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (34): 43-45.DOI: 10.3778/j.issn.1002-8331.2010.34.013

• 研究、探讨 • Previous Articles Next Articles

Cross validation model for neural network stability

QIU Long-jin¹，HE Chang-zheng²

1.College of Computer Science，Sichuan University，Chengdu 610065，China
2.College of Business Administration，Sichuan University，Chengdu 610065，China

Received:2010-03-12 Revised:2010-06-17 Online:2010-12-01 Published:2010-12-01
Contact: QIU Long-jin

神经网络稳定性的交叉验证模型

邱龙金¹，贺昌政²

1.四川大学计算机学院，成都 610065
2.四川大学工商管理学院，成都 610065

通讯作者: 邱龙金

Abstract

Abstract: According to cross-validation theory by Skutin，the cross-validation model of neural network stability is proposed.Four wildly used and representative neural networks are adopted as the subjects investigated and retrieved the rank of the stabilities of BP，RBF，GRNN，ELM，using the experiment results based on the random and UCI data sets.Finally，and the ranking is tested by the statistical method.

摘要： 根据Skutin提出的交叉验证理论，针对神经网络学习算法提出了神经网络稳定性的交叉验证模型，并选择4种应用广泛、具有代表性的神经网络作为研究对象，通过随机数据集和UCI数据集上的数据实验结果得出了BP、RBF、GRNN、ELM等4种神经网络的稳定性排序，并用统计检验方法对排序结果进行了检验。

CLC Number:

TP183

QIU Long-jin¹，HE Chang-zheng². Cross validation model for neural network stability[J]. Computer Engineering and Applications, 2010, 46(34): 43-45.

邱龙金¹，贺昌政². 神经网络稳定性的交叉验证模型[J]. 计算机工程与应用, 2010, 46(34): 43-45.

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Cross validation model for neural network stability

神经网络稳定性的交叉验证模型

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