基于Hopfield神经网络的毕业论文评价研究

Rong-jun Li, Xiangjun Wang, Dingyuan Y. Li
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引用次数: 0

摘要

毕业论文的评价需要多方面考虑,才能对各个因素做出最终的评价。传统的加权评价方法缺乏客观性,缺乏说服力。针对存在的问题,提出采用Hopfield法对毕业论文进行评价。通过已有的评价结果,得出了评价标准。评价标准存储在网络中。当向网络输入预评价论文时,Hopfield神经网络将自动运行。预评价论文将收敛到最接近的水平。实验采用10组数据进行。实验结果表明,该方法能够有效地对毕业论文进行评价,且评价结果更加快速、合理、客观。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Graduation Thesis Evaluation Based on Hopfield Neural Network
The evaluation of graduation thesis needs to consider from many aspects to make the final evaluation of each factor. The traditional evaluation method with weighted method has a loss of objectivity and lack of convincing. In view of the existing problems, it proposed that using Hopfield method to evaluate the graduation thesis. Through the existing evaluation results, the evaluation criteria were got. The evaluation criteria were stored in the network. When inputting the pre-evaluation thesis to the network, Hopfield neural network will run by itself. The pre-evaluation thesis will converge to the closest level. The experiments were carried out with 10 group's data. The experimental results show that this method can effectively evaluate the graduation thesis, and the evaluation results more rapid, rational and objective.
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