双创背景下基于SVM的虚拟装配系统装配性能分析预测研究  

Research on assembly performance analysis and prediction of virtual assembly system based on SVM under the background of entrepreneurship and innovation

作　　者：蔚燕舞[1] 郭润平[1] YU Yanwu;GUO Runping(Xi’an Siyuan College,Xi’an 710038,China)

机构地区：[1]西安思源学院,西安710038

出　　处：《自动化与仪器仪表》2025年第1期201-205,共5页Automation & Instrumentation

基　　金：23年教育部第一批协同育人项目《民办高校创新创业教育课程思政范式转型与实践》(230804643281141)。

摘　　要：为了提升虚拟装配系统装配性能预测的准确性,研究提出基于改进二叉树的支持向量机算法,并基于改进算法提出虚拟装配系统装配性能预测模型。对研究提出的改进算法进行性能对比实验,结果显示,该算法的准确率为90.8%,优于对比算法。之后对提出的预测模型进行验证发现,该模型的拟合度为0.9823,显著高于对比模型,且研究还发现该模型能够提高对创新创业的影响水平。上述结果说明,研究提出的预测模型的预测效果较好,不仅能够为创新创业领域提供技术支持,而且能够推动制造领域的发展。In order to improve the accuracy of assembly performance prediction for virtual assembly systems,a support vector machine algorithm based on improved binary tree was proposed,and a new assembly performance prediction model for virtual assembly systems was proposed based on the improved algorithm.The experimental results show that the accuracy of the improved algorithm is 90.8%,which is better than the comparison algorithm.After verifying the proposed prediction model,it is found that the fit degree of the model is 0.9823,which is significantly higher than the comparison model.Moreover,the study also finds that the model can improve the impact level on innovation and entrepreneurship.The above results show that the prediction model proposed in the study has good prediction effect,which can not only provide technical support for the field of innovation and entrepreneurship,but also promote the development of the manufacturing field.

关 键 词：创新 创业 虚拟装配系统 预测模型 DBT-SVM 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]