基于BP神经网络的大学生科研能力评价
发布时间：2018-07-13 16:34
【摘要】：在创新型国家建设和国家科技体制改革的背景下,为了适应国家科技发展趋势,高校对学生进行科研能力培养与评价是十分必要的。传统能力评价缺乏对评价指标的重要度判断,重要度判断过程具有随意性和主观性,针对以上问题,本文使用BP神经网络对大学生科研能力进行评价,旨在提高评价过程的科学性和精确性,完善大学生培养过程,为高校了解学生科研能力提供较为科学的依据。本文首先基于大学生科研能力培养的过程、论文写作流程,分析每个过程中涉及到的能力,构建了大学生科研能力评价指标体系;综合利用形成性评价和终结性评价的方式,设计了大学生科研能力指标评分细则;根据评分细则表设计了网络调查问卷,根据收集的问卷数据基于组合赋权法对指标赋权。然后针对评价指标的非线性特征,达到弱化传统评价方法存在的随机性和主观性的效果,实现能力评价的科学性和实用性,构建了基于BP神经网络的评价模型;为了加强训练样本的可比性,利用min-max方法对样本内容进行标准化处理;根据样本的选择规则,从问卷数据中选取了等量的样本用来训练和测试;利用试凑法确定了隐层神经元的数量;应用选取的样本对基于梯度下降法、拟牛顿法、列文伯格法(Levenberg-Marquardt,LM)的BP神经网络在matlab软件中进行实验,从网络均方误差、迭代次数、泛化能力、预测准确率等四个评价指标对3种BP神经网络模型针对本问题进行了可行性验证,实验结果表明基于LM算法的8-12-1的单隐层BP神经网络评价模型应用于大学生科研能力评价是可行的。最后对原型系统进行设计与实现,从需求分析、数据库设计、核心模块三个方面进行了阐述,设计并实现了 BP神经网络模型管理模块和能力评价模块,初步实现了评价大学生科研能力的原型系统,为大学生科研管理提供了实用的工具。
[Abstract]:Under the background of the construction of innovative country and the reform of national science and technology system, it is very necessary for colleges and universities to cultivate and evaluate students' scientific research ability in order to adapt to the development trend of national science and technology. The traditional ability evaluation lacks the importance judgment to the evaluation index, the important degree judgment process has the arbitrariness and the subjectivity, in view of the above question, this article uses the BP neural network to carry on the appraisal to the university student scientific research ability. The purpose of this paper is to improve the scientificity and accuracy of the evaluation process, to perfect the cultivation process of college students, and to provide a scientific basis for the understanding of students' scientific research ability in colleges and universities. Firstly, based on the process of cultivating college students' scientific research ability, the thesis writing process, analyzing the ability involved in each process, constructing the evaluation index system of university students' scientific research ability, synthetically utilizing the formative evaluation and summative evaluation methods. The evaluation rules of scientific research ability index of college students are designed, and the network questionnaire is designed according to the scoring rules table, and the index is weighted according to the collected questionnaire data based on the combination weight method. Then aiming at the nonlinear characteristics of the evaluation index, the evaluation model based on BP neural network is constructed by weakening the randomness and subjectivity of the traditional evaluation method and realizing the scientificity and practicability of the ability evaluation. In order to enhance the comparability of training samples, the min-max method is used to standardize the contents of the samples, and according to the selection rules of the samples, the same number of samples are selected from the questionnaire data for training and testing. The number of hidden layer neurons is determined by trial and error method, and the BP neural network based on gradient descent method, quasi-Newton method and Levenberg-Marquardt LM method is used to test the BP neural network in matlab software. Four evaluation indexes, such as generalization ability and prediction accuracy, are used to verify the feasibility of three BP neural network models. The experimental results show that the application of 8-12-1 single hidden layer BP neural network evaluation model based on LM algorithm to the evaluation of university students' scientific research ability is feasible. Finally, the prototype system is designed and implemented, including requirement analysis, database design and core module. The model management module and capability evaluation module of BP neural network are designed and implemented. The prototype system for evaluating the scientific research ability of college students is implemented preliminarily, which provides a practical tool for the management of university students' scientific research.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G642;TP183
本文编号：2120055
[Abstract]:Under the background of the construction of innovative country and the reform of national science and technology system, it is very necessary for colleges and universities to cultivate and evaluate students' scientific research ability in order to adapt to the development trend of national science and technology. The traditional ability evaluation lacks the importance judgment to the evaluation index, the important degree judgment process has the arbitrariness and the subjectivity, in view of the above question, this article uses the BP neural network to carry on the appraisal to the university student scientific research ability. The purpose of this paper is to improve the scientificity and accuracy of the evaluation process, to perfect the cultivation process of college students, and to provide a scientific basis for the understanding of students' scientific research ability in colleges and universities. Firstly, based on the process of cultivating college students' scientific research ability, the thesis writing process, analyzing the ability involved in each process, constructing the evaluation index system of university students' scientific research ability, synthetically utilizing the formative evaluation and summative evaluation methods. The evaluation rules of scientific research ability index of college students are designed, and the network questionnaire is designed according to the scoring rules table, and the index is weighted according to the collected questionnaire data based on the combination weight method. Then aiming at the nonlinear characteristics of the evaluation index, the evaluation model based on BP neural network is constructed by weakening the randomness and subjectivity of the traditional evaluation method and realizing the scientificity and practicability of the ability evaluation. In order to enhance the comparability of training samples, the min-max method is used to standardize the contents of the samples, and according to the selection rules of the samples, the same number of samples are selected from the questionnaire data for training and testing. The number of hidden layer neurons is determined by trial and error method, and the BP neural network based on gradient descent method, quasi-Newton method and Levenberg-Marquardt LM method is used to test the BP neural network in matlab software. Four evaluation indexes, such as generalization ability and prediction accuracy, are used to verify the feasibility of three BP neural network models. The experimental results show that the application of 8-12-1 single hidden layer BP neural network evaluation model based on LM algorithm to the evaluation of university students' scientific research ability is feasible. Finally, the prototype system is designed and implemented, including requirement analysis, database design and core module. The model management module and capability evaluation module of BP neural network are designed and implemented. The prototype system for evaluating the scientific research ability of college students is implemented preliminarily, which provides a practical tool for the management of university students' scientific research.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G642;TP183
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