基于径向基神经网络的数字馆藏质量评价研究  

Evaluation and Study of the Quality of Digital Collections Based on RBF Neural Network

作　　者：张秀华[1] 赵伟[2] 

机构地区：[1]鲁东大学图书馆,山东烟台264025 [2]鲁东大学网络中心,山东烟台264025

出　　处：《情报理论与实践》2009年第5期61-64,共4页Information Studies:Theory & Application

基　　金：鲁东大学校基金项目的研究成果;项目编号:W20072301

摘　　要：根据径向基神经网络的自组织、自学习和自适应等特性,提出了基于径向基神经网络的数字馆藏质量评价方法,建立了评价模型,运用该模型对山东省烟台和威海地区的5所高校图书馆的数字馆藏进行了质量评价。通过MATLAB仿真试验结果分析,证明了其可行性和有效性。According to the self-organizing, self-learning and self-adapting characteristics of RBF neural network, this paper describes a method for evaluating the quality of digital collections based on RBF neural network, and constructs an evaluation model. The model is used to evaluate the quality of digital collections of 5 university li- braries in Yantai and Weihai regions, Shandong Province. Analysis of the resuh of MATLAB simulation test proves that the model is feasible and effective.

关 键 词：径向基 神经网络 数字馆藏 质量评价 

分 类 号：G253[文化科学—图书馆学] G353.1