欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为提高磨粒智能识别的准确率,以传统支持向量机和粒子群优化（PSO）算法为基础,提出一种基于改进PSO算法的支持向量机（SVM）识别模型。该识别模型的惩罚参数和核函数参数可同时得到最佳优化,从而可建立模型参数最优的自适应SVM识别模型。采用该识别模型对油液中的磨粒进行智能识别,结果表明该模型识别准确率高达98%,明显优于BP神经网络模型。
英文摘要：
In order to improve the accuracy of wear debris intelligent recognition, a support vector machine（SVM） recognition model based on improved particle swarm optimization （PSO） algorithm is proposed on the basis of traditional SVM and PSO.The penalty parameter and kernel function parameters of the recognition model can be best optimized at the same time, so that self-adaptive SVM recognition model with optimal parameters was established.The wear debris in the lubricating oil was recognized is as high as 98%, whi by using the proposed recognition model.The resuhs show that the model recognition accuracy rate ch is significantly higher than the BP neural network model.
同期刊论文项目
同项目期刊论文
An Algorithm of Quantum Restricted Boltzmann Machine Network Based on Quantum Gates and Its Applicat
Engine fault diagnosis based on a morphological neural network using a morphological filter as a pre
期刊信息
位置：