摘要
本文以上海中医药大学的大学生为例,介绍了运用离散Hopfield人工神经网络和复杂系统熵分划两种方法构建“评价和预测”之平台,及时、客观、公正地评价和前瞻性地预测大学生基础素质的情况,这种模式可减少盲目无效的干预行为和过程,值得进一步推广。
This article takes the college students of Shanghai University of Traditional Chinese Medicine as an example,introduces the use of discrete Hopfield artificial neural network and complex system entropy division to build an "evaluation and prediction"platform to timely,objectively and fairly evaluate and prospectively predict the basic quality of college students Case.This model can reduce blind and ineffective interventions and processes,and is worthy of further promotion.
作者
朱政
张倩华
朱懿文
杨旭明
ZHU Zheng;ZHANG Qianhua;ZHU Yiwen;YANG Xuming(Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China;China Medical University,Shenyang 110122,China)
出处
《浙江医学教育》
2021年第4期8-10,共3页
Zhejiang Medical Education
基金
上海市中医药科技创新项目(编号:ZYKC201601002)
上海中医药大学第十三期大学生创新创业项目(编号:2020SHUTCM101)。
关键词
大学生素质
Hopfield人工神经网络
复杂系统熵分划
中医药大学
quality of college students’
hhopfield artificial neural network
entropy partitioning for complex systems
university of Traditional Chinese M edicine
