一类求解比例延迟积分微分方程线性多步法的散逸性

考虑了比例延迟积分微分方程的数值方法的散逸性。首先,通过变换将原方程变为常延迟积分微分方程,然后把一类线性多步法应用到以上问题中,用线性插值程序和复合梯形公式分别逼近延迟项和积分项,证明了在一定条件下,该数值方法具有散逸性。     

结构化环境下基于结构单元软编码的3维激光雷达点云描述子

在同时定位与地图构建（SLAM）系统中,基于3维激光雷达点云数据的闭环检测由于描述子计算困难而极具挑战．为此,本文提出一种结构化环境下可用于闭环检测的基于结构单元软编码的新型3维激光雷达点云描述子．针对3维激光雷达点云的稀疏性和独立性导致的3维空间线段提取困难的问题,首先通过几何滤波的方法提取3维空间中垂直于地面的线段,用于保留3维空间的结构信息;然后,基于线段的空间几何关系构建结构单元集合,并通过软编码技术计算特征向量,作为3维激光雷达点云的描述子;最后,通过两帧点云描述子的匹配实现闭环检测．在KITTI公开数据集和自采数据集上的对比实验,验证了本文方法在时效性和鲁棒性等方面均优于主流的3维激光闭环检测方法．     

可视化交互式遗传算法及其在图像感性检索中的应用

提出了一种可视化IGA模型，结合GA在n维空间上快速搜索的优点和人类在2维空间上把握数据整体分布的能力，采用可视化的方法使用户主动参与搜索过程以加快遗传算法的收敛速度，从而减轻用户疲劳．采用主元分析的方法将n维空间中的个体向量映射到2维空间．并显示出来，用户可以在这个2维空间中选择一个好的个体加入遗传过程．以此来加速算法的收敛．实验证明可视化IGA较一般的IGA有更快的收敛速度，对减轻用户疲劳有很好的作用．该模型用于图像的感性检索，取得了较好的检索结果。     

纯时滞两次ITAE优化控制系统的背驰定律

本文给出纯时滞系统新的二次优化控制算法， 即不论在有穷维空间，还是在无穷维空间，两次都用ITAE优化控制律进行优化．这种系统， 设定的一次优化超调δ1(μ)%愈小，二次优化超调δ2(μ)%愈大，系统的鲁棒 性愈强，称之为背驰定律．最后讨论一个算例，结果表明，鲁棒性强，对时变时滞系统的优 化控制，更具有工程实用价值．     

基于混合计算机接口的多旋翼飞行器3维空间目标搜索

提出一种融合半自主导航、决策与接口转换子系统实现多旋翼飞行器室内3维空间目标搜索的混合计算机接口系统．半自主导航子系统为决策子系统提供2维空间可行飞行方向并实现多旋翼飞行器3维空间半自主避障．决策子系统采用联合回归模型与谱功率法从6个电极所采集的运动想象脑电信号中提取时域与频域特征,并利用支持向量机完成分类．接口转换子系统采用连续小波变换检测眨眼时的眼电特征,并通过分析这些眼动特征实现水平与垂直方向的运动想象任务接口切换．实际的室内3维空间目标搜索实验验证了该系统具有较好的适应性与控制稳定性;相比其他方法,半自主导航子系统降低了控制难度,控制精度约提高±10 cm．     

引入形状统计先验的轮廓提取的概率方法

提出了轮廓提取的概率方法,同时将物体形状的统计先验信息和轮廓线的平滑性结合到轮廓的概率估计中．首先应用主元分析对低维空间概率采样生成高维形状统计分布的样本,根据形状样本得到轮廓控制点样本,而后结合描述平滑性的先验概率,运用序列蒙特卡罗方法实现轮廓估计．对实际拍摄的人手图像的实验表明,针对特定的形状,该方法在杂乱背景、遮挡和强噪声干扰情况下依然能够得到满意的物体轮廓线．     

有限维空间映射下SVM分类机的半正定核函数

本文针对支持向量分类机的特．置，分析了在有限维空间中满足特征空间映射的条件和结果。提出了有限维空间映射下满足半正定条件的核函数确定方法。指出，只要存在对称函数K，如果这个函数形成的Gram矩阵是半正定的，就必然能够将原输入空间映射成有限维的特征空间，从而在映射空间的内积可以用这个核函数K表示。     

基于地表的连续可见最近邻查询方法

连续可见最近邻查询是查询连续空间的最近邻问题,目前的研究基本以二维空间为背景并提出了一些查询算法,但可见性判断方法不能适用于三维或高维空间.以陆地表面的三维数据为研究背景,提出了一种查询地表任意路径的连续可见最近邻方法.该方法以计算步长的方式把整个查询路径分割成若干个连续的查询子路径,循环计算每个子路径的连续可见最近邻直至得到整个路径的查询结果.该方法可以扩展应用于高维空间中的连续最近邻查询.     

一种基于超图模式的高维空间数据聚类方法

本文把一个求解高维空间数据聚类问题转换为一个超图分割寻优问题,提出一种基于超图模式的高维空间数据聚类方法.该方法不需要减少高维空间数据项的维数,直接用超图模式描述原始数据之间的关系,并能通过选择适当的支持度阈值,有效去除噪声点,保证数据聚类的质量.     

基于位姿空间栅格扩展及变维搜索的\=机器人运动规划新策略

本文首先对机器人运动规划算法中的栅格扩展策略及次序规划法进行了探讨,并提 出了相应的改进方法,在此基础上提出了一种新的机器人运动规划策略——基于位姿空间栅 格扩展及变维空间搜索算法,该算法可有效地在复杂环境中找出无碰撞路径．     

Global dissipativity of memristor-based complex-valued neural networks with time-varying delays

Memristor is the new model two-terminal nonlinear circuit device in electronic circuit theory. This paper deals with the problem of global dissipativity and global exponential dissipativity for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays. Sufficient global dissipativity conditions are derived from the theory of M-matrix analysis, and the globally attractive set as well as the positive invariant set is established. By constructing Lyapunov–Krasovskii functionals and using a linear matrix inequality technique, some new sufficient conditions on global dissipativity and global exponential dissipativity of MCVNNs are derived. Finally, two numerical examples are presented to demonstrate the effectiveness of our proposed theoretical results.     

Extended dissipativity of generalised neural networks including time delays

This article explores the extended dissipativity conditions for generalised neural networks (GNNs) including interval time-varying delays. Extended dissipativity criterions are proposed by making proper Lyapunov–Krasovskii functional. The improved reciprocally convex combination and weighted integral inequality techniques are together applied in main results to establish the new extended dissipativity conditions of delayed GNNs. Finally, the feasibility and superiority of the proposed novel approach is clearly shown by numerical examples.     

Dissipativity of extended Pouzet–Runge–Kutta methods for neutral delay integro-differential equations

This paper is concerned with dissipativity of extended Pouzet–Runge–Kutta methods for neutral delay integro-differential equations. The finite-dimensional and infinite-dimensional dissipativity results of the (k, l)-algebraically stable extended Pouzet–Runge–Kutta methods are obtained under suitable conditions.     

Stochastic dissipativity analysis on discrete-time neural networks with time-varying delays

In this paper, the problems of global dissipativity and global exponential dissipativity are investigated for discrete-time stochastic neural networks with time-varying delays and general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing stochastic analysis technique, several new delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequalities (LMIs). Furthermore, when the parameter uncertainties appear in the discrete-time stochastic neural networks with time-varying delays, the delay-dependent robust dissipativity criteria are also presented. Two examples are given to show the effectiveness and less conservatism of the proposed criteria.     

Exponential quasi‐(Q,S,R)‐dissipativity and practical stability for switched nonlinear systems

In this paper, the problems of exponential quasi‐(Q,S,R)‐dissipativity and practical stability analysis for a switched nonlinear system are addressed. First, the concept of exponential quasi‐(Q,S,R)‐dissipativity for switched nonlinear systems without requiring the exponential quasi‐(Q,S,R)‐dissipativity property of each subsystem is proposed. Then, we show that an exponentially quasi‐(Q,S,R)‐dissipative switched nonlinear system is practically stable. Second, this exponential quasi‐(Q,S,R)‐ dissipativity property for a switched nonlinear system is obtained by the design of a state‐dependent switching law. Third, a composite state‐dependent switching law is designed to render the feedback interconnection of switched nonlinear systems exponentially quasi‐(Q,S,R)‐dissipative. This switching law allows interconnected switched nonlinear systems to switch asynchronously. Finally, the effectiveness of the results is verified by a numerical example.     

Exponentially incremental dissipativity for nonlinear stochastic switched systems

ABSTRACT

In this paper, we investigate the exponentially incremental dissipativity for nonlinear stochastic switched systems by using the designed state-dependent switching law and multiple Lyapunov functions approach. Specifically, using incremental supply rate as well as a state dissipation inequality in expectation, a stochastic version of exponentially incremental dissipativity is presented. The sufficient conditions for nonlinear stochastic switched systems to be exponentially incrementally dissipative are given by the designed state-dependent switching law. Furthermore, the extended Kalman–Yakubovich–Popov conditions are derived by using two times continuously differentiable storage functions. Moreover, the incremental stability conditions in probability for nonlinear stochastic switched systems are derived based on exponentially incremental dissipativity. The exponentially incremental dissipativity is preserved for the feedback-interconnected nonlinear stochastic switched systems with the composite state-dependent switching law; meanwhile, the incremental stability in probability is preserved under some certain conditions. A numerical example is given to illustrate the validity of our results.     

Dissipativity analysis of neural networks with time-varying delays

A new definition of dissipativity for neural networks is presented in this paper. By constructing proper Lyapunov functionals and using some analytic techniques, sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the obtained results.     

Dissipative stochastic differential systems with risk-sensitive storage function and control design problems

We consider stochastic control-affine systems defined by a differential Itô-type equation. A new notion of dissipativity with risk-sensitive storage function is introduced, and corresponding theory of dissipative systems is developed. Problems of finding storage function and control which ensures dissipativity are explicitly solved. In a linear-quadratic case the final results are expressed in terms of linear matrix inequalities. Connections to risk-sensitivity, differential (stochastic and deterministic) games, and deterministic ?_∞-control theory are established. An example is given.     

时滞依赖的广义线性系统QRS耗散性

利用构造储能函数的方法研究一类广义时滞线性系统的QRS耗散性问题,得到系统是QRS耗散的充分条件以及使得闭环系统是QRS耗散的状态反馈控制器的设计方法,结果具有线性矩阵不等式的形式,容易求解,用一个仿真示例验证结论的有效性。