期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Robustly stable model predictive control based on parallel support vector machines with linear kernel 被引量:4
1
作者 包哲静 钟伟民 +1 位作者 皮道映 孙优贤 《Journal of Central South University of Technology》 EI 2007年第5期701-707,共7页
Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs ... Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin. 展开更多
关键词 parallel support vector machines model predictive control stability ROBUSTNESS
下载PDF
Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification
2
作者 R.Sujitha B.Paramasivan 《Computers, Materials & Continua》 SCIE EI 2022年第1期1557-1572,共16页
With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus d... With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus disease 2019,and other diseases.In particular,our algorithm focuses on integrated datasets as compared with other existing works.In this study,parallel-based SVM(P-SVM)andmulticlass-basedmultiple submodels(MMSM-SVM)were used to analyze the optimal classification of lung diseases.This analysis aimed to find the optimal classification of lung diseases with id and stages,such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses,respectively.For nonlinear classification,kernel clustering-based SVM embedded with multiple submodels was developed.Both algorithms were developed using Apache spark environment,and data for the analysis were retrieved from microscope lab,UCI,Kaggle,and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB.Performance measures were conducted using a 5 GB dataset with five nodes.Dataset size was finally increased,and task analysis and CPU utilization were measured. 展开更多
关键词 Lung cancer COVID-19 machine learning deep learning parallel based support vector machine multiclass-based multiple submodel
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部