机构

• 数学与系统科学研究院 [2]
• 长春光学精密机械与物... [1]
• 武汉物理与数学研究所 [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2021 [1]
• 2019 [1]
• 2016 [1]
• 2012 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4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 Stability of steady-state for 3-D hydrodynamic model of unipolar semiconductor with Ohmic contact boundary in hollow ball 期刊论文  OAI收割 JOURNAL OF DIFFERENTIAL EQUATIONS, 2021, 卷号: 277, 页码: 57-113 作者:  Mei, Ming;  Wu, Xiaochun;  Zhang, Yongqian   |  收藏  |  浏览/下载：20/0  |  提交时间：2021/04/26 3-Dimensional hydrodynamic model  Euler-Poisson equations  Subsonic steady-state  Asymptotic stability  Radial solutions  Weighted Sobolev spaces   Classification of nonnegative solutions to a bi-harmonic equation with Hartree type nonlinearity 期刊论文  OAI收割 PROCEEDINGS OF THE ROYAL SOCIETY OF EDINBURGH SECTION A-MATHEMATICS, 2019, 卷号: 149, 期号: 4, 页码: 979-994 作者:  Cao, Daomin;  Dai, Wei   |  收藏  |  浏览/下载：28/0  |  提交时间：2020/01/10 bi-harmonic  nonnegative solutions  Liouville type theorems  radial symmetry  Hartree type nonlinearity  methods of moving planes   UNIQUENESS OF POSITIVE RADIAL SOLUTIONS TO SINGULAR CRITICAL GROWTH QUASILINEAR ELLIPTIC EQUATIONS 期刊论文  OAI收割 ANNALES ACADEMIAE SCIENTIARUM FENNICAE-MATHEMATICA, 2016, 卷号: 41, 期号: 1, 页码: 143-166 作者:  He, Cheng-Jun;  Xiang, Chang-Lin 收藏  |  浏览/下载：21/0  |  提交时间：2016/05/03 Quasilinear elliptic equations  singular critical growth  positive radial solutions  Pohozaev identity  uniqueness  asymptotic behaviors   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：22/0  |  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.