[1]关潮辉,丁树良.基于IRT模型的BP神经网络参数估计的进一步研究[J].江西师范大学学报(自然科学版),2014,(04):434-436.
　GUAN Chao-hui,DING Shu-liang.Further Research on the Paraneter Estination Method of BP Neural Network Based on Iten ResPonse Theory[J].Journal of Jiangxi Normal University:Natural Science Edition,2014,(04):434-436.
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基于IRT模型的BP神经网络参数估计的进一步研究()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2014年04期
页码:
434-436
栏目:
出版日期:
2014-08-31

文章信息/Info

Title:
Further Research on the Paraneter Estination Method of BP Neural Network Based on Iten ResPonse Theory
作者:
关潮辉;丁树良
江西师范大学计算机信息工程学院,江西 南昌,330022
Author(s):
GUAN Chao-hui；DING Shu-liang
关键词:
参数估计; 项目反应理论; BP 神经网络; 蒙特卡洛模拟
Keywords:
parameter estimation; IRT; BP neural network; Monte-Carlo simulation
分类号:
B841.7；TP301.6
文献标志码:
A
摘要:
对基于 IRT 模型的 BP 神经网络降维法参数估计中的 BP 神经网络的网络隐层数及隐层节点数进行改进，并对其降维法中不合理的部分予以修正。通过蒙特卡洛模拟研究结果表明：对参数估计的各评价指标，改进的方法均优于原方法。
Abstract:
Parameter estimation is one of the important components of item response theory(IRT). When the sample of examinees or the number of the items is small,the traditional statistical methods of parameter estimation may be failure. Due to the shortcoming of the traditional parameter estimation methods,a new way based on BP neural net-work with reduced dimension method was proposed. This article is a further research on the work exited. With a modified reduced dimension method and a new construction of BP neural network,the Monte Carlo study shows a better result.

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备注/Memo

备注/Memo:
国家自然科学基金(31360237，31300876，31160203，31100756，30860084)；国家社会科学基金(12BYY055，13BYY087)；江西省教育厅科技计划(GJJ 3207，GJJ 3208，GJJ 3209，GJJ13226，GJJ13227)
更新日期/Last Update: 1900-01-01