基于径向基函数神经网络的高校图书馆用户满意度评价模型

College Library User Satisfaction Evaluation Model Based on RBF Neural Network

如何客观准确的评价高校图书馆用户满意度是一个比较困难的问题。近年来,BP神经网络技术完全以客观数据为基础,可充分挖掘出潜在的有用信息,有效避免人为主观因素的影响,成为高校图书馆用户满意度评价的热点之一。相比传统BP神经网络,将学习速度更快、易于收敛的径向基函数神经网络技术应用于高校图书馆满意度评价中,重点论述了径向基函数神经网络评价模型的设计和实现,并通过实例分析验证了该模型的有效性。

How to evaluate the user satisfaction of college library objectively and accurately is always a difficult research topic.In recent years,BP neural network technology has become one of the hotspots in college library user satisfaction evaluation,which is fully based on objective data and exploits the potential useful information effectively and avoids the influence of subjective factors.Compared to the traditional BP neural network,RBF neural network can learn faster and converge more easily.This paper applied RBF neural network in the user satisfaction evaluation of college library,and expounded the design and implementation of evaluation model based on RBF neural network,and then analyzed the effectiveness of this model by example.