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国家自然科学基金(60973097)

作品数:5 被引量:11H指数:2
相关作者:谢志鹏陈松灿孙忠贵曹连连张丽梅更多>>
相关机构:南京航空航天大学华侨大学聊城大学更多>>
发文基金:国家自然科学基金更多>>
相关领域:自动化与计算机技术理学更多>>

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A new kind of nonlinear fractional-order chaotic phenomenon in coupled systems: coexistence of anti-phase and complete synchronization
2011年
In this paper, we have found a kind of interesting nonlinear phenomenon hybrid synchronization in linearly coupled fractional-order chaotic systems. This new synchronization mechanism, i.e., part of state variables are anti- phase synchronized and part completely synchronized, can be achieved using a single linear controller with only one drive variable. Based on the stability theory of the fractional-order system, we investigated the possible existence of this new synchronization mechanism. Moreover, a helpful theorem, serving as a determinant for the gain of the controller, is also presented. Solutions of coupled systems are obtained numerically by an improved Adams Bashforth-Moulton algorithm. To support our theoretical analysis, simulation results are given.
Zhang Jun-FengPei Qiu-YuZhang Xiao-Li
基于前向后向算子分裂的稀疏信号重构被引量:5
2012年
压缩感知包括压缩采样与稀疏重构.压缩采样突破了传统的香农采样定理限制,降低了采集数据量,是新兴的信号采集方法.稀疏重构算法是恢复原始高维信号的关键步骤,已成为信号处理及相关领域的研究热点.设计了一种稀疏重构算法FPSP3,该算法包含3个要素:不动点迭代,SPG2非单调线搜索及热启动技术.将非光滑L1范数罚最小二乘的最优解表示为梯度算子与次微分算子和的零点,采用前向后向算子分裂法推导出最优解方程为包括前向梯度步与后向邻近步的不动点迭代,通过证明后向邻近步对应L1范数的邻近算子即软阈值收缩,从而将不动点迭代表示为梯度下降与软阈值收缩.通过证明梯度算子逆是强单调的从而简化了收敛步长分析,给出了不动点迭代线性收敛于最优解的简要证明.采用SPG2非单调线搜索与热启动技术显著加快了算法实际运行速率,在稀疏重构实验中与某些著名的L1范数方法进行了比较,结果表明FPSP3具有运算速度与重构精度优势.
谢志鹏
关键词:压缩感知算子分裂不动点迭代非单调线搜索
CSMP:基于约束等距的压缩感知匹配追踪被引量:6
2012年
压缩感知包括压缩采样与稀疏重构,是一种计算欠定线性方程组稀疏解的方法.大规模快速重构方法是压缩感知的研究热点.提出一种匹配追踪算法CSMP,采用迭代式框架和最佳s项逼近以逐步更新信号的支集与幅度.基于约束等距性质进行收敛分析,算法收敛的充分条件为3s阶约束等距常数小于0.23,松弛了匹配追踪重构s稀疏信号的约束等距条件,加快了收敛速度.为适用于大规模稀疏信号重构,提供了可进行随机投影测量子集与稀疏基子集选择的矩阵向量乘算子,可利用离散余弦变换与小波变换,避免了大规模矩阵的显式存储.在220随机支集的稀疏高斯信号,512×512Lenna图像上进行压缩采样与稀疏重构实验并与其他算法进行比较,结果表明所提算法快速稳健,适用于大规模稀疏信号重构.
谢志鹏陈松灿
加权Laplacian分类器
2012年
传统的Laplacian分类器通过Parzen窗估计概率密度函数,而后基于Cauchy-Schwarz散度(Cauchy-Schwarz Divergence,CSD)定义分类准则进行分类,主要强调了小概率类别,致使对较高概率的类的分类效果较一般.针对此,提出了一个改进方法.关键是对小样本类用加权代替无权的Parzen窗概率密度估计,并用CSD作为代价函数优化相应的权值,而后依据Laplacian分类准则设计出加权Laplacian分类器(Weighted Laplacian Classifier,WLC).在所用测试数据集,尤其是不平衡数据集上的实验表明,WLC的结果明显优于Lapalcian分类器.
曹连连陈松灿
关键词:加权
SELF-DEPENDENT LOCALITY PRESERVING PROJECTION WITH TRANSFORMED SPACE-ORIENTED NEIGHBORHOOD GRAPH
2010年
Locality preserving projection (LPP) is a typical and popular dimensionality reduction (DR) method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP (sdLPP) is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.
乔立山张丽梅孙忠贵
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