摘要
为了提高远程教育系统的故障分析能力,研究结合了先进的大数据技术,利用人工鱼群算法对模糊聚类算法进行了优化,构建了一个高效的故障分析模型。对研究提出的改进模糊聚类算法进行对比实验,结果显示,该算法的准确率、绝对误差分别为99.0%、0.0027,优于基于密度的聚类算法和谱聚类算法。之后在故障分析模型的实证分析中发现,研究提出的远程教育系统故障分析模型在故障检测准确率上表现出显著优势,达到了95.3%,远超其他同类模型。上述结果说明,研究提出模型在远程教育系统故障分析中具有有效性,且其也能够为科学、技术、工程、数学远程教育领域的进一步发展提供技术支持。
In order to improve the fault analysis ability of distance education system,the paper combines advanced big data technology,uses artificial fish swarm algorithm to optimize fuzzy clustering algorithm,and builds an efficient fault analysis model.The results show that the accuracy and absolute error of the proposed improved fuzzy clustering algorithm are 99.0%and 0.0027 respectively,which is superior to the density-based clustering algorithm and the spectral clustering algorithm.Then,in the empirical analysis of the fault analysis model,it is found that the fault analysis model of the distance education system proposed in the study has a significant advantage in the fault detection accuracy rate,reaching 95.3%,far exceeding other similar models.The above results show that the proposed model is effective in fault analysis of distance education system,and it can also provide technical support for the further development of distance education in science,technology,engineering and mathematics.
作者
雷彬
LEI Bin(Xi’an Fanyi University,Xi’an 710105,China)
出处
《自动化与仪器仪表》
2024年第12期224-228,共5页
Automation & Instrumentation
基金
陕西省教育科学规划2023年度课题《“三位一体”背景下陕西高校教师数字素养提升策略研究》(SGH23Y2768)。
