基于智能优化算法的教学质量评价方法  

Teaching Quality Evaluation Based on Intelligent Optimization Algorithms

作　　者：朱娅妮[1] 

机构地区：[1]杭州电子科技大学工程研究院,浙江杭州310018

出　　处：《杭州电子科技大学学报（自然科学版）》2014年第6期66-70,共5页Journal of Hangzhou Dianzi University：Natural Sciences

基　　金：杭州电子科技大学高等教育研究资助项目(YB1343)

摘　　要：为了提高教学质量评价的准确性,提出了一种基于智能优化算法的教学质量评价方法。首先采用层次分析法构建评价指标体系,然后用多群体遗传算法优化设计BP神经网络,最后用BP神经网络评价教学质量。实验结果证明,该文提出的方法由于结合了神经网络和遗传算法的优点,克服了评价系统的盲目性,采用非线性逼近的方法提高了教学质量评价的准确率,是一种高效的评价方法。In order to improve the accuracy of college teaching quality evaluation, this paper puts forward a teaching quality evaluation method based on intelligent optimization algorithm. Firstly, the evaluation index system is established by the analytic hierarchy process. Secondly, multi-population genetic algorithm is used to optimize BP neural network. Finally, the optimized BP neural network is used to evaluate the teaching quality. The experiments results prove that the proposed method not only overcomes the blindness of the neural network, but also improve the evaluation accuracy in use of nonlinear approximation. The results show that the proposed method is feasible and effective.

关 键 词：层次分析法 多群体遗传算法 神经网络 教学质量评价 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] G420[自动化与计算机技术—控制科学与工程]