基于SOFM神经网络的学生综合评价

Comprehensive Evaluation of Students Based on SOFM Neural Networks

对学生的综合评价可以采用一系列可量化的指标来描述:智育素质、思想道德素质、身心素质、科学人文素质等,传统的对学生的评价很难综合考虑学生各方面的素质,从而导致评价不合理.为了能够综合评价学生各方面的素质,在提出改进的自组织特征映射(SOFM)神经网络的基础上,利用SOFM网络能够对高维数据有效分类的特点,将量化后的学生各方面的素质指标作为输入数据,在对样本数据进行训练后,根据输出神经元在输出层的位置对学生进行分类,最终把学生合理地分为优秀、良好、中等、稍差、差5个等级.

The comprehensive evaluation of students depends on several factors such as the intellectual quality,the ideological and moral quality,the physical and mental quality and the scientific and humanistic quality.The traditional evaluation of students is difficult to consider various aspects of students.To totally take account of various aspects of students,an improved selforganizing feature maps（SOFM） networks is proposed,which can map high-dimensional data into simple geometric relationships on a low-dimensional display effectively.The quantitative qualities of students are used as inputs to a SOFM.After giving some training,according to the location of the output neurons in the output layer,the students are finally classified into five categories by SOFM：excellent,good,general,less poor and poor.