基于最优参数实现 Lorenz-stenflo系统的相空间重构

摘要：以一个新的扩展 Lorenz系统为背景,利用向量间的相似度改进了相空间重构所需的最优延迟时间的算法,并最终实现了该系统的相空间重构。数值结果表明改进后的算法是有效的,可能更好选择最优延迟时间。     

信息熵和HQ准则在最大Lyapunov指数计算中的应用

最大Lyapunov指数是判断时间序列是否为混沌的一个重要判据,目前应用比较广泛的是小数据量法。将信息熵和HQ准则应用在最大Lyapunov指数的算法中,改进了小数据量法。信息熵优化了相空间重构参数,克服了独立求解重构参数的不足;利用HQ准则确定邻近点个数增加了计算时的精度。仿真实验表明该改进的小数据量法在计算最大Lyapunov时具有良好的准确性,对噪声具有良好的鲁棒性。     

螺旋桨鸣音的混沌动力特性研究

利用混沌动力学方法研究螺旋桨鸣音系统时间序列,估计时间序列的相空间重构最佳参数,并首次提出其具有混沌动力特性,还进一步分析了系统拓扑维数的边界和生成系统所必须地独立变量的个数,计算分析了重构相空间中吸引子轨迹随时间演化的发散情况。分析计算结果表明：螺旋桨鸣音系统时间序列可以选取最佳延迟时间 、最小嵌入维数 进行相空间重构,其混沌吸引子的关联维数为5.1579、最大Lyapunov指数为0.0771,此研究结果可以为螺旋桨鸣音现象的进一步研究提供理论基础。     

相空间重构中时间延迟τ的确定方法

非线性系统的相空间重构可以将时间序列中蕴藏的信息充分地显露出来，其中时间延迟τ的选取具有十分重要的意义，同时这种选取也是很困难的。文章介绍了相空间重构中确定重构延迟时间τ的互信息法理论及其具体的计算方法，最终实际的数据证实了该算法的合理性。     

两自由度碰撞振动系统的Lyapunov指数谱分析

摘要 针对一类两自由度碰撞振动系统,取碰撞前瞬时的定相位面为Poincaré截面,引入局部映射,构造Poincaré映射并给出其Jacobi矩阵.利用Gram-Schmidt正交化、范数归一化和迭代的方法,得出两自由度碰振系统Lyapunov指数谱的计算方法.利用数值模拟,讨论系统周期吸引子和混沌吸引子的Lyapunov指数谱的收敛序列,序列的收敛性很好. 为了验证该计算方法的正确性和有效性,分析当系统参数在大范围内变化时,相应的最大Lyapunov指数的变化规律.     

基于Lyapunov指数能谱分布的转子-机匣系统故障诊断研究

提出了基于Lyapunov指数能谱分布的航空发动机转子-机匣系统故障诊断新方法.基于相空间重构,在优选重构参数的基础上,计算得到了某型航空发动机不同故障状态下机匣实测振动信号时间序列的Lyapunov指数谱;提出并定义了Lyapunov指数能、Lyapunov指数能谱、Lyapunov指数能谱分布的概念,获得了某型航空发动机在不同故障状态下的机匣实测振动信号时间序列的Lyapunov指数能谱及其分布,并基于此Lyapunov指数能谱分布对航空发动机转子-机匣系统进行了故障诊断和状态识别.研究结果表明:航空发动机机匣振动时间序列在不同状态下具有不同的Lyapunov指数能谱及其分布,即可以Lyapunov指数能谱及其分布作为识别其状态的特征量,为航空发动机转子-机匣系统的故障诊断和状态监控提供了新的可靠的方法.     

自适应局部独立分量分析

提出一种自适应局部独立分量分析降噪算法。该算法先将一维时间序列重构到高维相空间,用聚集模糊K均值聚类和聚类评价函数求取高维数据集的聚类个数和聚类中心位置,然后利用K均值聚类寻找局部投影区间,对每个聚类进行独立分量分析并投影到低维空间,将低维空间数据排列并重构成一维时间序列。与使用聚类的局部独立分量分析相比,该算法具有自适应性和稳定性。使用数值仿真试验和齿轮故障信号对该算法进行验证,结果表明该算法对此类信号具有良好的降噪效果。     

确定相空间重构参数的一种新方法

用相点的惯量矩来描述相轨迹在相空间中展开的程度,用相点的惯量矩的相对变化来描述相轨迹展开的快慢。导出了相点关于主对角线的、与重构参数有关的惯量矩公式。用该公式计算了相点的惯量矩的相对变化,选出了不同维数的延迟时间。从非零延迟时间中选出的最小维数就是嵌入维数,该非零延迟时间就是最终确定的延迟量。用此方法获得的参数重构出的相轨迹质量较好,且具有抗噪能力强。     

EMD端点效应的改进型混沌延拓方法及其在机械故障诊断中的应用

针对经验模态分解(Empirical Mode Decomposition,EMD)的端点效应问题,分析了三次样条插值产生端点效应的机理,指出了现有延拓方法的不足,提出采用Lyapunov指数预测模型进行边界延拓,以此减小端点效应在经验模态分解过程中产生的影响。在采用最大Lyapunov指数法进行边界延拓时,针对Lyapunov指数预测模型所表示的平均发散度仅是轨道演化规律的一个近似模型,提出采用局域发散度代替最大Lyapunov指数进行改进,以改进预测模型的预测精度。基于改进型Lyapunov指数边界延拓方法,由于引入时间序列预测模型使端点处的延拓更加合理,所延拓数据更加趋于真实,在希尔伯特-黄变换（Hilbert-Huang Transform,HHT）实现过程中仅需一次延拓,实现了准确的EMD分解。仿真计算和转子系统故障试验分析结果表明,所用方法可以有效解决EMD的端点效应问题。     

奇异谱分解在超声速无人机声振试验数据处理中的应用

将相空间重构和奇异谱分解相结合对受强气动噪声影响的超声速飞行器测试数据进行滤波,以实现对于试验参数的精确识别。首先通过数值仿真论证该方法的可行性,然后针对某型超声速无人机的声振试验对试验采集数据进行相空间重构,并对重构后的轨迹矩阵进行奇异值分解,得到反映真实信号信息的信号子空间和反映噪声信息的噪声子空间。通过定义奇异值差分谱这一指标来判定真实信号信息子空间维数,并针对现有最大差分谱理论缺陷提出了优选差分谱峰值理论,利用奇异谱分解的逆过程对真实信号进行重构。重构结果表明,该方法适用于超声速飞行下的飞行器声振试验数据处理,为超声速飞行器飞行状态的精确描述提供了良好的思路。     

Phase-space prediction of chaotic time series

We report on improved phase-space prediction of chaotic time series. We propose a new neighbour-searching strategy which corrects phase-space distortion arising from noise, finite sampling time and limited data length. We further establish a robust fitting algorithm which combines phase-space transformation, weighted regression and singular value decomposition least squares to construct a local linear prediction function. The scaling laws of prediction error in the presence of noise with various parameters are discussed. The method provides a practical iterated prediction approach with relatively high prediction performance. The prediction algorithm is tested on maps (Logistic, Hénon and Ikeda), finite flows (Rössler and Lorenz) and a laser experimental time series, and is shown to give a prediction time up to or longer than five times the Lyapunov time. The improved algorithm also gives a reliable prediction when using only a short training set and in the presence of small noise.     

Receive maximal-ratio combining with outdated arbitrary transmit antenna selection in Nakagami-m fading

The use of transmit antenna selection algorithms in multiple-antenna systems enables significant reduction in implementation cost and complexity while maintaining acceptable performance. An attractive and quite flexible selection algorithm is to allow the receiver to pick any of the transmit antennas that can satisfy a predetermined performance target. Such an algorithm is referred to as the arbitrarily ordered transmit antenna selection algorithm. However, the effectiveness of transmit antenna selection is decreased by several propagation impairments over the feedback channel from the receiver to the transmitter. Of these impairments, the feedback channel time delay may impose a significant impact on the achieved performance. This paper aims to investigate the impact of this time delay on the performance of receive maximal-ratio combining (MRC) diversity employing the arbitrarily ordered transmit antenna selection algorithm. In order to obtain quantitative measures for this impact, new expressions for various performance criteria are obtained by using the new derived formulas for the probability density function (pdf) and the moment generating function (MGF) of the combined signal-to-noise ratio (SNR). Numerical and simulation results are presented to illustrate the effect of delayed (i.e. outdated) feedback information on the system performance for various transmit antenna selection scenarios.     

Single- and double-difference algorithms for position and time-delay calibration of transducer-elements in a sparse array

A method for the calibration of the position and time delay of transducer elements in a large, sparse array used for underwater, high-resolution, ultrasound imaging has been described in a previous work. This algorithm is based on the direct algorithm used in the global positioning system (GPS), but the wave propagation speed is treated as one of the to-be-calibrated parameters. In this article, the performance of two other commonly used GPS algorithms, namely the single-difference algorithm and the double-difference algorithm, is evaluated. The calibration of the propagation speed also is integrated into these two algorithms. Furthermore, a novel, least-squares method is proposed to calibrate the time delay associated with each transducer element for these two algorithms. The performances of these algorithms are theoretically analyzed and evaluated using numerical analysis and simulation study. The performance of the direct algorithm, the single-difference algorithm, and the double-difference algorithm is compared. It was found that the single-difference algorithm has the best performance among the three algorithms for the current application, and it is capable of calibrating the position and time delay of transducer elements to an accuracy of one-tenth of a wavelength.     

Asymptotic Lyapunov stability with probability one of Duffing oscillator subject to time-delayed feedback control and bounded noise excitation

The asymptotic Lyapunov stability with probability one of a Duffing system with time-delayed feedback control under bounded noise parametric excitation is studied. First, the time-delayed feedback control force is expressed approximately in terms of the system state variables without time delay. Then, the averaged Itô stochastic differential equations for the system are derived by using the stochastic averaging method and the expression for the Lyapunov exponent of the linearized averaged Itô equations is derived. It is inferred that the Lyapunov exponent so obtained is the first approximation of the largest Lyapunov exponent of the original system, and the asymptotic Lyapunov stability with probability one of the original system can be determined approximately by using the Lyapunov exponent. Finally, the effects of time delay in feedback control on the Lyapunov exponent and the stability of the system are analyzed. The theoretical results are well verified through digital simulation.     

An Entity-Association-Based Matrix Factorization Recommendation Algorithm

Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user’s social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users’ interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first detect the community structure using overlapping community discovery algorithm, and mine the clustering information of user interest preference by a fuzzy clustering algorithm based on the project category information. On the other hand, we use project-category attribution matrix and user-project score matrix to get project comprehensive similarity and compute project feature matrix based on Entity Relation Decomposition. Fusing the user clustering information and project information together, we get Entity-Association-based Matrix Factorization (EAMF) model which can be used to predict user ratings. The proposed algorithm is compared with other algorithms on the Yelp dataset. Experimental studies show that the proposed algorithm leads to a substantial increase in recommendation accuracy on Yelp data set.     

Generation of Low-Delay and High-Stability Multicast Tree

Delay and stability are two key factors that affect the performance of multicast data transmission in a network. However, current algorithms of tree generation hardly meet the requirements of low delay and high stability simultaneously. Given a general network, the generation algorithm of a multicast tree with minimum delay and maximum stability is an NP-hard problem, without a precise and efficient algorithm. To address these challenges, this paper studies the generation of low-delay and high-stability multicast trees under the model of spanning tree based on stability probability, degree-constrained, edge-weighted for multicast (T-SDE). A class of algorithms was proposed which creates the multicast tree greedy on the ratio of fan-out to delay (RFD) and probability of stability of terminal to obtain a high performance in multicast. The proposed algorithms greedily select terminals with a large RFD and a high probability of stability as forwarding nodes in the generation of the multicast tree, where the larger RFD and higher stability of upstream nodes are beneficial to achieve a low transmission delay and high stability in multicast. The proposed RFD can be compatible with the original model, which can take advantage of network connectivity during the generation of a multicast tree. This paper carries out simulation experiments on Matlab R2016b to measure the performance of the proposed algorithm. Experimental results show that the proposed algorithm can provide a smaller height, higher stability, and a lower transmission delay of the resulting multicast tree than other solutions. The spanning tree of the proposed algorithms can support low transmission delay and high stability in multicast transmission.     

基于DNSS与点到平面的ICP结合的点云配准算法

针对现有点云配准算法众多、配准速度和配准精度不尽相同的问题,本文提出了一种将DNSS与点到平面的ICP相结合的配准算法,利用DNSS提取源点云数据的关键点,利用关键点约束查找对应匹配点对,结合点到平面的误差度量方法计算最优刚体变换矩阵,从而完成点云配准。对配准后的结果进行误差分析,实验结果证明,基于DNSS与点到平面ICP结合的点云配准算法配准精度高于点到点的ICP算法和点到平面的ICP算法,且该方法处理几何特征复杂、特征明显的点云数据优势显著。     

Genetic robust controller design for uncertain systems with time‐delays in both state and control input via LMI approach

Abstract

In this paper, the robust control for uncertain delay systems in state and control input is considered. The uncertainty is nonlinear time‐varying perturbations and does not require a matching condition but is norm‐bounded. Based on the constructive use of Lyapunov functional, delay‐dependent stabilizability criteria are proposed to guarantee stability for the systems via linear control. Linear matrix inequality (LMI) and Genetic algorithms are used to design a memoryless state‐feedback controller. Two numerical examples are given to illustrate that the proposed results are less conservative than some others.