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：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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