Zhengrong Deng, Hong Xiang, Weijun Tang, Hanlie Cheng, Qiang Qin
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引用次数: 0
Abstract
This paper employs BP Neural Network (BPNN) theory to evaluate innovation and entrepreneurship education in universities. It utilizes students' evaluation indexes as input vectors and determines the number of hidden layer neurons. Experimental results serve as output vectors. The BPNN method proves reasonable and feasible for vocational education course evaluation, exhibiting a 14.96% higher accuracy than traditional genetic algorithms. The paper discusses the model, configuration, characteristics, training process, algorithm enhancement, and limitations of neural networks, followed by an introduction to genetic algorithms. Through analysis of principles, basic operations, and common operators, it establishes a theoretical foundation for subsequent discussions.
本文采用 BP 神经网络（BPNN）理论对高校创新创业教育进行评价。它利用学生的评价指标作为输入向量，并确定隐层神经元的数量。实验结果作为输出向量。事实证明，BPNN 方法在职业教育课程评价中是合理可行的，其准确率比传统遗传算法高出 14.96%。本文讨论了神经网络的模型、配置、特点、训练过程、算法改进和局限性，然后介绍了遗传算法。通过对原理、基本操作和常用运算符的分析，为后续讨论奠定了理论基础。
期刊介绍：
IJICTE publishes contributions from all disciplines of information technology education. In particular, the journal supports multidisciplinary research in the following areas: •Acceptable use policies and fair use laws •Administrative applications of information technology education •Corporate information technology training •Data-driven decision making and strategic technology planning •Educational/ training software evaluation •Effective planning, marketing, management and leadership of technology education •Impact of technology in society and related equity issues
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