面向国际一流学科的机械工程课程结构神经网络分析

Research on the Course Structure of International First-Class Mechanical Engineering Using Neural Network

本文从创新型人才培养的知识结构出发,以机械工程学科课程体系为研究对象,基于QS世界大学排名相关参数指标,使用现代数学分析和建模方法,建立了三层BP神经网络分析模型,并在MATLAB上对样本数据进行了训练和数据验证,模型误差集中于-0.6—+0.5。基于该模型定量地求出了QS排名系统未列出的中国某些高校的QS参数。对比分析结果表明,高校机械学科课程体系结构是决定其在QS系统排名的重要影响因素。本研究对于机械工程学科进入国际一流学科和培养创新型人才具有借鉴作用。

Mechanical engineering course system was taken as the research object,based on QS World University rankings relevant parameters,in order to cultivate innovative talents,used modem mathematical analysis method, established a three layer BP neural network analysis model,errors have been lumped in -0.6 ＋0.5. Based on this model,some Chinese universities,which are not listed in the QS rankings system,were quantitatively calculated. The results of comparative analysis show that the structure of the mechanical course system in universities is an important influence factor to the rankings of the QS system. This study is useful for the mechanical engineering disciplines to enter the international first-dass disciplines and cultivate innovative talents.