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• 沈阳自动化研究所 [4]
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• 数学与系统科学研究院 [2]
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• OAI收割 [18]

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• 会议论文 [8]
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• 2021 [1]
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浏览/检索结果: 共18条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Research on Fault Diagnosis Method of Rod Pumping Wells Based on CNN_APRCSO_SVM 会议论文  OAI收割 Chicago, May 26-28, 2021 作者:  Wang MX(王明新);  Zang CZ(臧传治);  Ji ZP(纪振平);  He YP(贺云鹏);  Zeng P(曾鹏)   |  收藏  |  浏览/下载：44/0  |  提交时间：2021/08/21 Rod pumping well  Fault diagnosis  Indicator diagrams  Convolutional neural network (CNN)  Support vector machine (SVM)  Chicken swarm optimization (CSO)  Algorithm optimization   Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine 期刊论文  OAI收割 MOLECULES, 2020, 卷号: 25 作者:  Chen, Yunan;  Yang, Ruifang;  Zhao, Nanjing;  Zhu, Wei;  Chen, Xiaowei   |  收藏  |  浏览/下载：93/0  |  提交时间：2020/12/28 concentration-emission matrix  PCA  genic algorithm  SVM  oil species recognition   RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction 期刊论文  OAI收割 RNA BIOLOGY, 2017, 卷号: 14, 期号: 7, 页码: 952-962 作者:  Chen, Xing;  Wu, Qiao-Feng;  Yan, Gui-Ying   |  收藏  |  浏览/下载：25/0  |  提交时间：2018/07/30 Disease  disease semantic similarity  KNN algorithm  miRNAs  miRNA-disease association  SVM Ranking model   Multivariate Mean Shift Diagnostic Model Based on Support Vector Machine 会议论文  OAI收割 Hawaii, USA, July 31 - August 4, 2017 作者:  Cai YJ(蔡亚军);  Wang Y(王宇);  Chen SH(陈书宏)   |  收藏  |  浏览/下载：41/0  |  提交时间：2018/10/08 Quality Monitoring  Quality Characteristics  Principal Component Analysis(Pca)  Support Vector Machine(Svm)  Modified Grid Algorithm   Design of human-computer interaction control system based on hand-gesture recognition 会议论文  OAI收割 32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017, Hefei, China, May 19-21, 2017 作者:  Liu JG(刘金国);  Zhao ZQ(赵梓淇);  Wang ZH(王志恒);  Cao, Jiangtao   |  收藏  |  浏览/下载：30/0  |  提交时间：2017/08/20 Human-Computer Interaction control system  sample robot  gesture recognition  improved PSO-SVM algorithm   SEMG based movement quantitative estimation of joins using SVM method with gravitational search algorithm 会议论文  OAI收割 27th Chinese Control and Decision Conference, CCDC 2015, Qingdao, China, May 23-25, 2015 作者:  Zhao XG(赵新刚);  Liu, Dongsheng 收藏  |  浏览/下载：17/0  |  提交时间：2015/11/18 sEMG  motion estimation  SVM  gravitational search algorithm  rehabilitation robot   基于GA-SVM回归的成矿有利度预测方法探讨/Discussion on Prediction Method for the Contribution Degrees to the Metallogenesis Based on GA-SVM Regression[J] 期刊论文  OAI收割 新疆地质, 2014, 卷号: 4, 页码: 546-551 作者:  李东;  周可法;  孙卫东;  王金林;  吴艳爽   |  收藏  |  浏览/下载：28/0  |  提交时间：2017/12/29 成矿预测 遗传算法(Ga) 支持向量机(Svm) 非线性拟合 Metallogenesis Prediction Genetic Algorithm (Ga) Support Vector Machine (Svm) Nonlinear Fitting   Application of GA-SVM method with parameter optimization for landslide development prediction 期刊论文  OAI收割 NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2014, 卷号: 14, 期号: 3, 页码: 525-533 作者:  Li, X. Z.;  Kong, J. M. 收藏  |  浏览/下载：37/0  |  提交时间：2014/12/02 Landslide prediction  Support vector machine (SVM)  Genetic algorithm (GA)  GA-SVM  parameter optimization   A SVM-kNN method for quasar-star classification 期刊论文  OAI收割 SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY, 2013, 卷号: 56, 期号: 6, 页码: 1227-1234 作者:  Peng NanBo;  Zhang YanXia;  Zhao YongHeng 收藏  |  浏览/下载：13/0  |  提交时间：2016/11/17 classification  stars/quasars  algorithm: SVM  kNN  data analysis   Classification of hyperspectral image based on SVM optimized by a new particle swarm optimization (EI CONFERENCE) 会议论文  OAI收割 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012, June 1, 2012 - June 3, 2012, Nanjing, China 作者:  Gao X.;  Yu P.;  Yu P. 收藏  |  浏览/下载：20/0  |  提交时间：2013/03/25 Support Vector Machine (SVM) is used to classify hyperspectral remote sensing image in this paper. Radial Basis Function (RBF)  which is most widely used  is chosen as the kernel function of SVM. Selection of kernel function parameter is a pivotal factor which influences the performance of SVM. For this reason  Particle Swarm Optimization (PSO) is provided to get a better result. In order to improve the optimization efficiency of kernel function parameter  firstly larger steps of grid search method is used to find the appropriate rang of parameter. Since the PSO tends to be trapped into local optimal solutions  a weight and mutation particle swam optimization algorithm was proposed  in which the weight dynamically changes with a liner rule and the global best particle mutates per iteration to optimize the parameters of RBF-SVM. At last  a 220-bands hyperspectral remote sensing image of AVIRIS is taken as an experiment  which demonstrates that the method this paper proposed is an effective way to search the SVM parameters and is available in improving the performance of SVM classifiers. 2012 IEEE.