基于降维状态观测器的轮轨力估计方法研究

轮轨作用力对车辆的运行品质及安全有重要影响;现有轮轨力测量方法采用了特制的测力轮对测量和轨道静态测量,其成本高、周期长、稳定性差;对现有轮轨力测量方法进行了研究,提出了降维状态观测器的方法观测轮轨力;建立单轮对车辆轨道垂向耦合模型,以车辆状态响应值与轨道激励作为降维状态观测器的输入;通过Matlab仿真计算,得出轮对振动位移的观测值快速收敛于仿真值,轮轨力有很好的吻合性;随着车辆速度增加振动位移收敛时间变长,轮轨力误差增大;结果表明,在一定条件下,此方法能够很好的地观测轮轨力的大小,为轮轨力的实时估计提供一种的新方法。     

成对约束降维集成下的MicroRNA预测

MicroRNA(miRNA)是一类在生物体内发挥重要调控作用的非编码小RNA,对miRNA的预测有助于研究和理解其生物学功能。已经提出的基于成对约束的降维算法(local semi-supervised linear discriminant analysis,LSLDA)在对miRNA降维的同时,也能保持数据的局部结构信息和判别能力,可有效改进miRNA的预测性能。因此,在LSLDA算法基础上,提出了一种新的集成LSLDA算法(ensemble of local semi-supervised linear discriminant analysis,En-LSLDA)。该算法对不同约束个数下的分类结果进行集成,以集成结果作为最后的分类结果,以此进一步改进miRNA的预测性能。miRNA数据集上的实验结果表明,En-LSLDA算法是有效可行的。同时,UCI数据集上的实验结果也验证了新提出的集成方法同样适用于其他数据集。     

通讯访问约束条件下线性随机系统的状态可估计性

本文从信息论的视角考察了线性离散随机系统在访问约束情况下的可估计性问题.针对系统中存在的访问约束所带来的通讯冲突现象,采用一个二值向量函数描述系统中各个输出信号的通讯状态.针对这类对象,根据由互信息定义的可估计性概念,讨论了访问约束系统的状态估计问题,得出了在输出检测信号传输存在访问约束情况下的状态可估计性条件,解析分析和仿真结果表明,存在合理的通信序列,使得系统在访问约束情况下,能够保持其状态可估计性.本文还证明了在访问约束条件下随机系统的可估计性和相应的确定性系统的能观性之间的等价关系.     

广义离散随机线性系统的降阶估计

该文讨论广义离散随机线性系统的状态估计问题，给出了一种降阶估计方法，该方法可直接估计系统的部分状态或系统状态的某个线性组合．     

起重机吊重系统降维观测器设计与仿真

研究起重机吊重系统稳定性控制问题,由于吊重过程产生摆动,起重机吊重系统防摇控制技术中的摆角和摆角速度信息难以准确测量,影响吊重的安全性。为此提出设计了起重机吊重系统降维状态观测器,给出了状态观测器的结构和增益矩阵,实现对摆角和摆角速度的状态信息重构。观测器能够分别在3.5s和1s内估计出系统的摆角和角速度,而且有较强的鲁棒性,实现防摇控制。由于观测器增益参数对观测器极点变化具有不同程度的敏感性,极点越大,收敛速度越快,观测时间越短。但是极点过大也会导致误差出现急剧峰值,不利于防摇实现。仿真结果表明,合理选择极点和设计降维观测器结构能够很好实现状态信息的软测量控制问题。     

能量约束无线传感器网络节能策略与状态估计协同研究

随着传感器技术的快速发展以及在各行业的广泛应用,能量约束问题逐渐显现,成为制约无线传感器网络技术发展与应用的瓶颈之一,因此,网络环境中的状态估计必须考虑网络能耗所带来的新问题。首先从提高网络能效、延长网络寿命的角度,讨论了无线传感器网络中能量消耗的主要因素和已有的几种节能策略,包括优化介质访问控制(MAC)技术、数据融合技术与节能路由协议等。进一步,基于对状态估计问题的考虑,给出分布式传感器网络中的状态估计框架图,在考虑节点失效、链接失败等原因的基础之上,分析了基于节约能耗的传感器调度策略及无线传感器网络系统中存在的不确定性问题。最后,总结能量约束下分布式状态估计进一步研究的关注点。     

基于直接估计梯度思想的数据降维算法

高维非线性数据的降维处理对于计算机完成高复杂度的数据源分析是非常重要的。从拓扑学角度分析,维数约简的过程是挖掘嵌入在高维数据中的低维线性或非线性的流形。该文在局部嵌入思想的流形学习算法的基础上,提出直接估计梯度值的方法,从而达到局部线性误差逼近最小化,实现高维非线性数据的维数约简,并在Swiss roll曲线上采样测试取得了良好的降维效果。     

基于辅助输出的线性系统状态和未知输入同时估计方法

在未知输入观测器匹配条件不满足的情况下, 针对一类线性时不变系统, 研究了同时估计系统 状态和未知输入的问题. 首先, 基于可测输出对未知输入的相关度的概念, 给出了辅助输出 的构造方法, 使得匹配条件得以满足. 为了处理辅助输出中的未知信息, 提出了一种高增益 观测器设计方法, 它不仅能估计辅助输出, 而且还能估计辅助输出的导数. 然后, 基于辅助 输出的估计值, 提出了一种降维观测器设计方法, 可以在不受未知输入影响的情况下估计系统 的状态; 接下来, 基于状态和辅助输出及其导数的估计值, 给出了未知输入估计. 最后, 对一个五 阶系统进行了数字仿真, 仿真结果表明所提出的方法是有效的.     

具有不确定动态线性系统的鲁棒状态估计

本文研究了一类具有参数和噪声特性不确定线性系统的鲁棒状态估计问题。利用对策论思想,定义了能使不确定下最坏性能最好的极小极大鲁棒状态估计器,提出了一种简单的近似设计方法,即设计最坏对象的最优滤波器。给出了这种设计方法设计滤波器导致的性能误差边界,进一步指出当满足文中给出的鞍点条件时,最坏对象的最优滤波器就是极小极大鲁棒滤波器。     

高维数据固有维数的自适应极大似然估计

如何确定高维数据的固有维数是降维成功与否的关键。基于极大似然估计（MLE）的维数估计方法是一种新近出现的方法,实现简单,选择合适的近邻能取得不错的结果。但当近邻数过小或过大时,均有比较明显的偏差。其根本原因是没有考虑每个点对固有维数的不同贡献。在充分考虑数据集的分布信息之后,提出了一种改进的MLE——自适应极大似然估计(AMLE)。实验表明,无论在合成数据集还是真实数据集上,AMLE较MLE在估计准确度上均有很大的提高,对近邻数的变化也不甚敏感。     

跳变约束下马尔可夫切换非线性系统滤波

针对系统状态演化多模不确定性和状态约束多样性,本文提出了跳变约束下马尔可夫切换非线性系统的交互式多假设估计方法.定义了包含跳变马尔可夫参数可能取值的假设集,根据最优贝叶斯滤波,推导出状态与假设的后验概率递推更新.基于统计线性回归线性化非线性函数,利用伪量测法,将线性化的约束扩维到真实量测中,给出了非线性系统滤波的近似解析最优解.最终给出所提算法的稀疏网格积分近似最优估计实现.在交叉道路机动目标跟踪仿真场景中,所提算法的滤波精度优于基于泰勒展开的交互式多模型算法,基于统计线性回归的交互式多模型算法,以及基于泰勒展开的非线性系统约束滤波算法.     

Optimal linear estimation for networked systems with communication constraints

This paper is concerned with the optimal linear estimation problem for discrete time-varying networked systems with communication constraints. The communication constraint considered is that only one network node is allowed to gain access to a shared communication channel, then the various network nodes of the networked systems are scheduled to transmit data according to a specified media access control protocol, and a remote estimator performs the estimation task with only partially available measurements. The channel accessing processes of those network nodes are modeled by Bernoulli processes, and optimal linear filters are designed by using the orthogonal projection principle and the innovation analysis approach. It is shown that the optimal estimation performances critically depend on the channel accessing probabilities of the network nodes and the packet loss probability, and the optimal filters can be obtained by solving recursive Lyapunov and Riccati equations. An illustrative example is finally given to show the effectiveness of the proposed filters.     

Optimal guaranteed cost filtering for uncertain discrete-time linear systems

This paper presents a result on the design of a steady-state robust state estimator for a class of uncertain discrete-time linear systems with normal bounded uncertainty. This result extends the steady state Kalman filter to the case in which the underlying system is uncertain. A procedure is given for the construction of a state estimator which minimizes a bound on the state error covariance. It is shown that this leads to a state estimator which is optimal with respect to a notion of quadratic guaranteed cost state estimation.     

广义离散随机线性系统降阶Wiener滤波、平滑和预报器

应用现代时间序列方法 ,基于自回归滑动平均 (ARMA)新息模型、白噪声估值器和观测预报器 ,对于广义离散随机线性系统 ,提出了降阶Wiener状态估值器 ,可统一处理滤波、平滑和预报问题 ,并且能减少计算负担 .仿真例子说明了算法的有效性     

Risk-sensitive filtering for jump Markov linear systems

In this paper, a risk-sensitive multiple-model filtering algorithm is derived using the reference probability methods. First, the approximation of the interacting multiple-model (IMM) algorithm is identified in the reference probability domain. Then, the same type of approximation is used to derive the finite-dimensional risk-sensitive filtering algorithm. The derived algorithm reduces to the IMM filter when the risk-sensitive parameter goes to zero and reduces to the risk-sensitive filter for linear Gauss-Markov systems when the number of models is unity. The algorithm performs better in a simulated uncertain parameter scenario than the IMM filter.     

Optimal carbon reduction level and ordering quantity under financial constraints

Carbon tax policy is widely adopted by many countries to curb carbon emissions. In the context of carbon tax policy, firms have more incentive to improve carbon reduction levels by reducing their carbon tax costs. However, firms need to bear carbon reduction costs that may cause shortage of capital. Thus, firms may face problems of financial constraints, which may demotivate firms to produce greener products. To address the decision‐making challenges of firms in the contexts of carbon tax policy and financial constraints, we consider a supply chain with a manufacturer who produces green products and a retailer who sells these products. Our study develops five models to investigate the two firms’ optimal wholesale price, carbon reduction level and ordering quantity, according to the manufacturer and retailer with or without financial constraints. Our goal in this study is to explore how carbon tax policy and banks’ interest rates affect the profits of the two firms, supply chain and consumer surplus. Certain managerial insights are obtained as follows. We demonstrate that carbon tax policy and banks’ interest rates demotivate the manufacturer to produce greener products and demotivate the retailer to order more products. If the interest rate to the manufacturer (retailer) is relatively low, then the manufacturer with financial constraint benefits (harms) the consumers compared with the retailer with financial constraint. Importantly, our analysis suggests that carbon tax policy harms the firms but benefits consumers, and the government in some conditions should reduce unit carbon tax.     

Consensus in multi‐agent systems with communication constraints

The problem of second‐order consensus is investigated in this paper for a class of multi‐agent systems with a fixed directed topology and communication constraints where each agent is assumed to share information only with its neighbors on some disconnected time intervals. A novel consensus protocol designed based on synchronous intermittent local information feedback is proposed to coordinate the states of agents to converge to second‐order consensus under a fixed strongly connected topology, which is then extended to the case where the communication topology contains a directed spanning tree. By using tools from algebraic graph theory and Lyapunov control approach, it is proved that second‐order consensus can be reached if the general algebraic connectivity of the communication topology is larger than a threshold value and the mobile agents communicate with their neighbors frequently enough as the network evolves. Finally, a numerical example is simulated to verify the theoretical analysis. Copyright © 2011 John Wiley & Sons, Ltd.     

A variable reduction strategy for evolutionary algorithms handling equality constraints

Efficient constraint handling techniques are of great significance when Evolutionary Algorithms (EAs) are applied to constrained optimization problems (COPs). Generally, when use EAs to deal with COPs, equality constraints are much harder to satisfy, compared with inequality constraints. In this study, we propose a strategy named equality constraint and variable reduction strategy (ECVRS) to reduce equality constraints as well as variables of COPs. Since equality constraints are always expressed by equations, ECVRS makes use of the variable relationships implied in such equality constraint equations. The essence of ECVRS is it makes some variables of a COP considered be represented and calculated by some other variables, thereby shrinking the search space and leading to efficiency improvement for EAs. Meanwhile, ECVRS eliminates the involved equality constraints that providing variable relationships, thus improves the feasibility of obtained solutions. ECVRS is tested on many benchmark problems. Computational results and comparative studies verify the effectiveness of the proposed ECVRS.