Consistent estimation of the memory parameter for nonlinear time series

Abstract. For linear processes, semiparametric estimation of the memory parameter, based on the log‐periodogram and local Whittle estimators, has been exhaustively examined and their properties well established. However, except for some specific cases, little is known about the estimation of the memory parameter for nonlinear processes. The purpose of this paper is to provide the general conditions under which the local Whittle estimator of the memory parameter of a stationary process is consistent and to examine its rate of convergence. We show that these conditions are satisfied for linear processes and a wide class of nonlinear models, among others, signal plus noise processes, nonlinear transforms of a Gaussian process ξ_t and exponential generalized autoregressive, conditionally heteroscedastic (EGARCH) models. Special cases where the estimator satisfies the central limit theorem are discussed. The finite‐sample performance of the estimator is investigated in a small Monte Carlo study.     

多机传动机械系统的同步控制

本文提出通过磁场定向控制分别控制多个感应电动机跟踪同一个指令性速度的多机驱动机械系统的同步控制方案,通过建立全维转子状态估计器,辩识感应电动机的转子速度,定子和转子电阻,瞬时外负载,根据外负载辩识结果与速度ＰＩＤ控制器确定感应电动机的转矩电流,实现感应电动机的无速度传感器控制,计算机仿真结果表明了该控制方案的有效性。     

Efficient Multiagent Policy Optimization Based on Weighted Estimators in Stochastic Cooperative Environments

Multiagent deep reinforcement learning (MA-DRL) has received increasingly wide attention. Most of the existing MA-DRL algorithms, however, are still inefficient when faced with the non-stationarity due to agents changing behavior consistently in stochastic environments. This paper extends the weighted double estimator to multiagent domains and proposes an MA-DRL framework, named Weighted Double Deep Q-Network (WDDQN). By leveraging the weighted double estimator and the deep neural network, WDDQN can not only reduce the bias effectively but also handle scenarios with raw visual inputs. To achieve efficient cooperation in multiagent domains, we introduce a lenient reward network and scheduled replay strategy. Empirical results show that WDDQN outperforms an existing DRL algorithm (double DQN) and an MA-DRL algorithm (lenient Q-learning) regarding the averaged reward and the convergence speed and is more likely to converge to the Pareto-optimal Nash equilibrium in stochastic cooperative environments.     

基于极大后验估计和指数加权的自适应UKF滤波算法

针对传统Unscented卡尔曼滤波器(Unscented Kalman filter, UKF)在噪声先验统计未知时变情况下非线性滤波精度下降甚至发散的问题, 设计了一种带噪声统计估计器的自适应UKF滤波算法. 首先根据极大后验(Maximum a posterior, MAP)估计原理, 推导出一种次优无偏MAP常值噪声统计估计器; 接着在此基础之上, 采用指数加权的方法, 给出了时变噪声统计估计器的递推公式; 最后对自适应UKF算法进行了性能分析. 相比于传统UKF, 该自适应UKF算法在噪声统计未知时变情况下不仅滤波依然收敛, 滤波精度及稳定性显著提高, 而且其具有应对噪声变化的自适应能力. 仿真实例验证了其有效性.     

图像集的离散度矩阵及其在人脸图像识别中的应用

本文提出了图像集离散度矩阵的基本概念及用于人脸图像识别的方法。该方法把同一类图像集中的各幅图像的信息矩阵的奇异值向量作为矩阵中的一列构成图像集特征矩阵。把这些矩阵组成一图像库,并找出图像集离散度矩阵和图像库离散度矩阵,从而计算出它们的最佳投影系数。然后,把测试样本的图像集特征矩阵与图像集库中的训练样本图像集的特征按投影系数相比较找出它的相似程度,从而进行人脸图像识别。     

Feature‐based recursive observer design for homography estimation and its application to image stabilization

This paper presents a new algorithm for online estimation of a sequence of homographies applicable to image sequences obtained from robotic vehicles equipped with vision sensors. The approach taken exploits the underlying Special Linear group structure of the set of homographies along with gyroscope measurements and direct point‐feature correspondences between images to develop temporal filter for the homography estimate. Theoretical analysis and experimental results are provided to demonstrate the robustness of the proposed algorithm. The experimental results show excellent performance and robustness even in the case of very fast camera motions (relative to frame rate) and severe occlusions.     

Nonparametric estimation of a probability density function and its derivatives. <Emphasis Type="Italic">C</Emphasis>-approach

Nonparametric (kernel) estimation of a probability density function f(x) for a sample of finite size is considered using the C-approach. The smoothness parameter β of the estimated probability density is introduced. For the case β > 2, it is shown that the convergence of the density estimate f _n (x) to the function f(x) can be improved by using alternating-sign weight functions (higher-order weight functions). Estimation of the derivatives of a function is briefly considered using the same approach.     

辨识网络拓扑错误的最小绝对值状态估计

采用最小绝对值状态估计算法辨识网络拓扑错误。在传统的最小二乘法状态估计模型中引进扩展变量和量测量,构成最小绝对值状态估计问题,进而转化为线性规划的标准形式求解。在一实际测验系统上进行了试算。     

内外不确定因素同时估计与补偿的鲁棒分布式跟踪控制

本文研究了多运动体系统中领航者(leader)和跟随者(follower)同时受扰条件下的跟踪控制问题.针对3–DOF直升机模型的角度轨迹协同跟踪任务,设计并验证了基于跟随者不确定因素和外部领航者干扰同时补偿的分布式鲁棒算法.分别通过不确定与干扰估计器和扩张状态观测器实现对内外扰动的估计与补偿,恢复了标称系统的跟踪性能...     

Estimator-based control of reactive distillation system: Application of an extended Kalman filtering

This paper demonstrates that a state estimator can be successfully designed and implemented in a feedback control system of reactive distillation. The work of the state estimator is to provide the state compositions that are required to be used in the controller for necessary action. The control performance of a system that relies on the state estimator is examined and compared to a system that takes direct measurement from the process assuming the availability of a perfect online analyzer. It is found that the estimator-based system is robust against a moderate measurement errors and erroneous initial conditions. If the state estimator is designed from a highly erroneous process model, noisy measurements and approximate initial conditions, the use of estimator together with an online analyzer (for easily measured states) is recommended to achieve an effective control of a reactive distillation system.