On sequential Monte Carlo sampling methods for Bayesian filtering

In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed. We show in particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature; these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.     

改进的动态粒子群算法

为更好的解决动态优化问题,提出改进的粒子群算法（Improved Partic le Swarm Optim ization,IP-SO）,在算法中对pBest替代策略引入了随机化算子,并且对群体中的gBest引入了局部搜索,对随时间变化的最优值进行了精确定位,试验证明该算法对动态优化问题的有效性.     

Style in a school community—“Ne” deletion in French preschool

This article explores the question of style in a school community, through negation use among the adults and children in a French preschool. Thirteen adults and 61 pupils aged 3–6 years were followed over a period of 2.5 years. The findings draw on a corpus of oral data collected in an unsupervised manner and comprising almost 640,000 transcribed words (428,100 for the children and 210,241 for the adults). The findings show, on the one hand, that the teachers adapt stylistically to their interlocutors and that their professional stance leads to more formal speech and, on the other hand, that the children's use of bipartite negation is marginal before the age of 6 years and does not present notable stylistic variations.     

Continuous-time threshold autoregressions with jumps: Properties,estimation, and application to electricity markets

Continuous-time autoregressive processes have been applied successfully in many fields and are particularly advantageous in the modeling of irregularly spaced or high-frequency time series data. A convenient nonlinear extension of this model are continuous-time threshold autoregressions (CTAR). CTAR allow for greater flexibility in model parameters and can represent a regime switching behavior. However, so far only Gaussian CTAR processes have been defined, so that this model class could not be used for data with jumps, as frequently observed in financial applications. Hence, as a novelty, we construct CTAR processes with jumps in this paper. Existence of a unique weak solution and weak consistency of an Euler approximation scheme is proven. As a closed form expression of the likelihood is not available, we use kernel-based particle filtering for estimation. We fit our model to the Physical Electricity Index and show that it describes the data better than other comparable approaches.     

超声多信号融合表征颗粒两相流实验研究

从颗粒两相流超声波动理论出发,提出多信号融合概念,采用单次和多次反射法同时测量颗粒两相流中高频宽带超声波声阻抗、声速和声衰减谱,获取了颗粒两相流系统中密度、浓度、粒径信息.基于该理论研制了可用于同时测量高浓度颗粒两相流密度、浓度、粒径的装置.高浓度水煤浆颗粒两相流测量结果显示,测量密度、浓度与平均粒径与标准对比较为吻合.     

群集智能研究进展

群集智能是指众多行为简单的个体相互作用过程中涌现产生的整体智能行为.从复杂系统研究的角度入手,以群集智能的基本原理为线索,对其系统结构、运行机理、建模工具、算法模型和典型应用等内容进行全面论述.首先围绕以蚁群和鸟群为代表的群集智能系统结构,分析说明了其中的个体属性、行为规则和交互方式,进而阐述和剖析了群集智能中的反馈机制和学习机制.在给出若干常用群集智能建模工具介绍的基础上,对蚁群觅食、蚁群聚类、蚁群劳动分工和鸟群觅食等4类群集智能模型进行了细致深入的探讨,旨在归纳提炼形成基于群集智能的复杂系统建模与仿真的一般性规律.最后综述了群集智能在工程优化、生产管理、机器人学、数据分析与模式识别等领域的典型应用情况并展望了群集智能的发展前景.