摘要
针对评估指标之间复杂的非线性关系,提出了一种基于改进粒子群小波神经网络评估教学质量的数学模型,该模型的参数由PSO优化。实验结果表明,PSO-WNN均方差为0.0281,通过使实际输出值和期望输出值的均方差较小,该方法可以更好地提高教学质量评估目标的准确性。同时,在应用于计算机实训教学质量评估中取得了良好的效果。
In view of the complex nonlinear relationship between the evaluation indexes,a mathematical model based on Improved PSO wavelet neural network is proposed to evaluate the teaching quality.The parameters of the model are optimized by PSO.The experimental results show that the mean square deviation of PSO-WNN is 0.0281.By making the mean square deviation of actual output value and expected output value smaller,this method can better improve the accuracy of teaching quality evaluation objectives.At the same time,it achieves good results in the evaluation of computer training teaching quality.
作者
秦轶翚
QIN Yi-hui(Beijing Union University Teachers College,Beijing 100011 China)
出处
《自动化技术与应用》
2021年第3期86-91,共6页
Techniques of Automation and Applications
基金
工业互联网知识教育研究(编号2017YFB0803600)。
关键词
粒子群算法
小波神经网络
计算机实训
Particle Swarm Optimization
Wavelet Neural Network
computer training
