A Generalized Markov Sampler

A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis–Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.     

基于专家动态权重的群组AHP交互式决策方法

为了能在群组决策中得到更为客观和准确的决策结果,在把专家权重划分为静态权重和动态权重的基础上,研究了在交互式决策中专家动态权重的确定方法,给出了共识度的一个定义.在此基础上,研究了群组AHP交互式决策方法中的一致性和相容性检验,给出了基于专家动态权重的群组AHP交互式决策方法流程,最后用一个示例说明了该方法的应用步骤.     

Sampling Consensus of 2nd-Order Multi-Agent Systems Based on Time-Varying Topology北大核心CSCD

基于速度一致位移差保持不变的一致性概念,研究了二阶多智能体系统在时变拓扑下的采样一致性问题。首先,引入虚拟领导者,将具有时变拓扑结构的多智能体系统的采样一致性问题转换为误差系统的采样控制稳定性问题。其次,通过预估采样误差,研究采样误差对系统达到一致性的影响。最后,应用Lyapunov稳定性理论,分析所构造的误差系统的稳定性,并给出该误差系统最终稳定的充分条件。数值仿真结果验证了理论分析的有效性和正确性。     

正确认识改革的代价是凝聚改革共识的必要条件

为了更好地凝聚改革共识，必须正确认识改革的代价问题。要改革就不可避免地会付出一定的代价，但改革过程中出现的各种问题并非都是改革的产物；改革代价的表现形式是多种多样的，不同形式的代价对改革与发展所起的作用也不同。改革主体能够能动地调控代价。为此，必须树立“底线思维”，牢牢把握改革代价的合理限度；提高改革决策的科学性；坚持主导性和全面性相统一、预测性和补偿性相结合的改革原则。     

基于邻域贡献权值细化的圆心亚像素定位算法

针对传统的圆心算法过程复杂、定位精度受初始边缘提取效果影响较大等问题,提出了一种基于邻域贡献权值细化的圆心亚像素定位算法。首先引入邻域贡献权值系数,改进传统非极大值抑制法,细化边缘;然后在边缘点的梯度方向对灰度值进行高斯拟合,确定亚像素边缘位置;最后针对边缘突变点提出了基于随机抽样一致的最小二乘法来拟合圆心。实验结果表明,该算法具有较好的精度和稳定性,圆心的提取精度可以达到0.1个像素。     

异质多智能体系统在固定拓扑下的分组一致性

研究了异质多智能体系统的分组一致性,针对固定通信拓扑情况,提出了一种基于邻接信息的分布式控制协议,并通过李雅普诺夫理论,推导出异质多智能体系统实现分组一致性的充分条件.最后利用数值仿真验证了理论分析的正确性.     

Stability analysis of a class of uncertain switched systems on time scale using Lyapunov functions

This paper deals with the stability analysis of a class of uncertain switched systems on non-uniform time domains. The considered class consists of dynamical systems which commute between an uncertain continuous-time subsystem and an uncertain discrete-time subsystem during a certain period of time. The theory of dynamic equations on time scale is used to study the stability of these systems on non-uniform time domains formed by a union of disjoint intervals with variable length and variable gap. Using the concept of common Lyapunov function, sufficient conditions are derived to guarantee the asymptotic stability of this class of systems on time scale with bounded graininess function. The proposed scheme is used to study the leader–follower consensus problem under intermittent information transmissions.     

Dynamic output feedback consensus of continuous‐time networked multiagent systems

This article addresses the dynamic output feedback consensus problem of continuous‐time networked multiagent systems. Both a fixed topology and Markovian switching topologies are considered. The consensus algorithms are on the base of the output information of each agent's itself and its neighbors. Some sufficient conditions for consensus of multiagent systems are obtained in forms of bilinear matrix inequalities. The algorithm based on the homotopy continuation method is given to compute the feasible controller gains. Numerical simulations are given to show the effectiveness of the proposed results. © 2014 Wiley Periodicals, Inc. Complexity 20: 35–42, 2015