基于改进粒子群算法的自优化考试系统

IF 2.4 Q2 ENGINEERING, MECHANICAL
Xiangran Du, M. Zhang, Yu-Lin He
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引用次数: 0

摘要

人工智能已经成功地应用于许多领域,节省了大量的人力和物力。智能考试系统是一个典型的应用案例,它使教师不仅可以随时掌握每个考生的学习情况,还可以借助考试系统设计进一步的学习计划。本文提出了一种基于改进粒子群算法的自优化检测系统。智能考试系统可以克服传统考试系统在建设中所表现出的两个困难,一是试题属性的设置,二是试题数据库的维护。实验表明,该方法不仅可以智能地优化试题数据库中的试题属性,而且可以通过大规模训练有效地维护试题数据库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-optimization examination system based on improved particle swarm optimization
Abstract Artificial intelligence has been applied to many fields successfully and saved many human and material resources. The intelligent examination system is a typical application case, which makes teachers can not only master the study situation of every candidate at any time but also design further study plans with the help of the examination system. A self-optimization examination system is shown in this paper, which is carried out by an improved particle swarm optimization. The intelligent examination system can surmount two difficulties shown in the construction of the traditional examining system, one is the setting of the attributes of the examination questions, and another is the maintenance of the database of the examination questions. The experiment shows that the novel method can not only optimize the attributes of the questions in the examination database intelligently but also maintain the examination database effectively through massive training.
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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