基于离散Hopfield神经网络的本科毕业论文质量评价研究

Study on the quality evaluation method for undergraduate thesis based on discrete Hopfield neural network

针对当前高校本科毕业论文质量评价易受诸多人为因素影响而产生的不合理现象，本研究从论文选题、专业知识水平、资料收集能力、知识运用能力、指导教师水平和论文书写质量六个维度，构建了较为科学、客观的质量递阶层次评价模型。采用专家打分法，结合Matlab编程，应用层次分析法获得了毕业论文质量评价要素的权重，确定了影响毕业论文质量水平的关键要素；建立了毕业论文质量水平的离散Hopfield神经网络模型；抽取120份毕业论文进行质量水平仿真评价．并与理想的等级评价结果比较。研究表明，基于离散Hopfield神经网络的方法，可以有效对本科毕业论文质量提供客观、科学和规范的评价，具有广泛的应用价值。

In terms of the poor evaluation system of graduation theses, which is easy to be influenced by many human factors, we try to establish a more scientific and objective evaluation model and method of graduation thesis quality. Firstly, the index system of graduation thesis quality evaluation model was con- structed, including topic selection, professional knowledge level, data collection capacity, ability to use knowledge, level of guidance teacher, and quality of the paper. Secondly, using expert scoring method, combining Matlab programming and ＇applying AHP, the weight of the quality evaluation elements of gradua- tion thesis was obtained, and the key factors affecting the quality level of graduation thesis were determined. Finally, a discrete Hopfield neural network model for the quality level of graduation thesis was established, and 120 graduation theses were selected for quality level simulation evaluation, and compared with the ideal evaluation results. The results showed that the discrete Hopfield neural network model is correct and it can effectively provide objective, scientific and normative evaluation for the quality of graduation theses.