知识共享效率的R-RNN评价模型研究及应用  

Evaluation of knowledge-sharing efficiency based on R-RNN

作　　者：李晓利[1] 杨育[1] 杨洁[1] 王伟立[1] 曾强[1] 宋李俊[1] 

机构地区：[1]重庆大学机械工程学院,重庆400030

出　　处：《计算机工程与应用》2009年第10期226-230,共5页Computer Engineering and Applications

基　　金：国家自然科学基金No.70601037;重庆市科技攻关计划No.2007AC2039~~

摘　　要：针对企业知识共享效率评价方法缺乏的现状,提出了基于粗糙集和RBF神经网络的R-RNN知识共享效率评价模型。在研究知识共享活动基本过程的基础上,分析了知识共享效率影响因素,得出效率评价指标体系。然后,运用粗糙集理论对评价指标进行预处理,去除冗余指标项,在合理化评价指标体系的同时减少网络输入维度,进而采用RBF神经网络对知识共享效率进行综合评价。最后通过具体的应用实例验证了该评价模型的有效性与可行性。Because of the lack of efficiency evaluation method,the knowledge-sharing efficiency evaluation model based on rough sets and RBF neural network（Rough Set and Radial Basis Function Neural Network,R-RNN） is proposed in this paper.Firstly,the process of knowledge-sharing is researched,the efficiency affecting factors of knowledge-sharing is analyzed,and then the index system of efficiency evaluation is constructed.Secondly,the theory of rough set is utilized to rationalize the knowledge-sharing efficiency evaluation index system and reduce the input dimensionality of RBF neural network.Then,RBF neural network is used to get the synthetic evaluation value of knowledge-sharing efficiency.Finally,an application example is given to validate the feasibility and effectiveness of the model.

关 键 词：知识共享 效率评价 指标体系 粗糙集 RBF神经网络 

分 类 号：C93[经济管理—管理学]