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微粒群神经网络在绩效评价系统中的应用
引用本文:李庆昌,刘希玉.微粒群神经网络在绩效评价系统中的应用[J].计算机工程与应用,2008,44(24):219-221.
作者姓名:李庆昌  刘希玉
作者单位:山东师范大学 管理与经济学院,济南 250014
基金项目:山东省自然科学基锺大项目,山东省教育厅资助科研课题,泰山学者建设工程专项经费资助项目 
摘    要:绩效评价系统是人力资源系统3P模型中的重要一环,是定期考察和评价个人或小组工作业绩的一种正式制度。利用微粒群算法对神经网络进行训练,再将此网络模型应用到人力资源管理系统中的绩效评价系统。最后通过在各级评价标准内按随机均匀分布方式生成的训练样本和测试样本来检测该微粒群神经网络。结果表明微粒群神经网络具有较强的泛化能力,应用在绩效评价系统中具有很高的评价准确率。

关 键 词:微粒群优化算法  神经网络  绩效评价  
收稿时间:2007-10-26
修稿时间:2007-12-29  

Applying particle swarm optimization algorithm-based artificial neural network to performance evaluation system
LI Qing-chang,LIU Xi-yu.Applying particle swarm optimization algorithm-based artificial neural network to performance evaluation system[J].Computer Engineering and Applications,2008,44(24):219-221.
Authors:LI Qing-chang  LIU Xi-yu
Affiliation:Department of Management and Economics,Shandong Normal University,Jinan 250014,China
Abstract:Performance evaluation system is an important part of the human resource performance evaluation system,which is a formal system of regular inspection and evaluation of individual or group work performance,is an important part of 3P model in human resources system.This paper uses PSO algorithm to train neural network.And then this network model is applied to performance evaluation system of the human resources management system.Finally,using training samples and test samples which are generated randomly by evaluation criteria at all levels within the uniform distribution methods,can detect the particle swarm neural network.Results show that the PSO neural network has strong generalization ability,and that application in the performance evaluation system has very high accuracy rate.
Keywords:particle swarm optimization algorithm  artificial neural network  performance evaluation
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