IDENTIFICATION OF TIME-VARYING MODAL PARAMETERS USING LINEAR TIME-FREQUENCY REPRESENTATION

A new method of parameter identification based on linear time-frequencyrepresentation and Hubert transform is proposed to identity modal parameters of linear time-varyingsystems from measured vibration responses. Using Gabor expansion and synthesis theory, measuredresponses are represented in the time-frequency domain and modal components are reconstructed bytime-frequency filtering. The Hilbert transform is applied to obtain time histories of the amplitudeand phase angle of each modal component, from which time-varying frequencies and damping ratios areidentified. The proposed method has been demonstrated with a numerical example in which a lineartime-varying system of two degrees of freedom is used to validate the identification scheme based ontime-frequency representation. Simulation results have indicated that time-frequency representationpresents an effective tool for modal parameter identification of time-varying systems.

LMS-LIKE ESTIMATION FOR TIME VARYING PARAMETERS

An LMS-like algorithm is applied for estimating the time-varying parameter theta-n in the linear model y(n) = phi-n-tau-theta-n + upsilon-n, which is general in the sense that none of the probabilistic properties such as stationarity, Markov property, independence and ergodicity is imposed on any of the processes {y(n)}, {phi-n}, {theta-n} and {upsilon-n}. It is shown that the alpha-th moment of the estimation error is of order of the alpha-th moment of the observation noise and the parameter variation w(n) change in equivalence theta-n - theta-n-1.

Identification of Time-Varying Modal Parameters for Thermo-Elastic Structure Subject to Unsteady Heating

A time-varying modal parameter identification method combined with Bayesian information criterion(BIC)and grey correlation analysis(GCA)is presented for a kind of thermo-elastic structures with sparse natural frequencies and subject to an unsteady temperature field.To demonstrate the method,the thermo-elastic structure to be identified is taken as a simply-supported beam with an axially movable boundary and subject to both random excitation and an unsteady temperature field,and the dynamic outputs of the beam are first simulated as the measured data for the identification.Then,an improved time-varying autoregressive(TVAR)model is generated from the simulated input and output of the system.The time-varying coefficients of the TVAR model are expanded as a finite set of time basis functions that facilitate the time-varying coefficients to be time-invariant.According to the BIC for preliminarily determining the scope of the order number,the grey system theory is introduced to determine the order of TVAR and the dimension of the basis functions simultaneously via the absolute grey correlation degree(AGCD).Finally,the time-varying instantaneous frequencies of the system are estimated by using the recursive least squares method.The identified results are capable of tracking the slow time-varying natural frequencies with high accuracy no matter for noise-free or noisy estimation.

TECHNICAL STABILITY OF NONLINEAR TIME-VARYING SYSTEMS WITH SMALL PARAMETERS

Technical stability:allowing quantitative estimation of trajectory behavior of a dynamical system over a given time interval was considered. Based on a differential comparison principle and a basic monotonicity condition, technical stability relative to certain prescribed state constraint sets of a class of nonlinear time-varying systems with small parameters was analyzed by means of vector Liapunov function method. Explicit criteria of technical stability are established in terms of coefficients of the system under consideration. Conditions under which the technical stability of the system can be derived from its reduced linear time-varying (LTV) system were further examined, as well as a condition for linearization approach to technical stability of general nonlinear systems. Also, a simple algebraic condition of exponential asymptotic stability of LTV systems is presented. Two illustrative examples are given to demonstrate the availability of the presently proposed method.

A New Discrete-time Adaptive ILC for Nonlinear Systems with Time-varying Parametric Uncertainties

用在分离时间轴和反复的学习轴之间的类比，一条新分离时间的适应反复的学习控制(AILC ) 途径被开发与变化时间的参量的无常探讨非线性的系统的一个班。类似于适应控制，新 AILC 能合并一个设计算法，因此，学习获得能沿着学习的轴反复地被调节。当起始的状态是随机的，参考轨道是变化重复的时，新 AILC 能沿着反复的学习轴 asymptotically 在有限时间间隔上完成 pointwise 集中。

Synchronization-based approach for parameter identification in delayed chaotic network

This paper introduces an adaptive procedure for the problem of synchronization and parameter identification for chaotic networks with time-varying delay by combining adaptive control and linear feedback. In particular, we consider that the equations xi（t） （for i = r＋ 1, r＋2,... ,n） can be expressed by the former xi（t） （for i=1,2,...,r）, which is not the same as the previous equation. This approach is also able to track changes in the operating parameters of chaotic networks rapidly and the speed of synchronization and parameter estimation can be adjusted. In addition, this method is quite robust against the effect of slight noise and the estimated value of a parameter fluctuates around the correct value.

Improved delay-dependent globally asymptotic stability of delayed uncertain recurrent neural networks with Markovian jumping parameters

In this paper, we have improved delay-dependent stability criteria for recurrent neural networks with a delay varying over a range and Markovian jumping parameters. The criteria improve over some previous ones in that they have fewer matrix variables yet less conservatism. In addition, a numerical example is provided to illustrate the applicability of the result using the linear matrix inequality toolbox in MATLAB.

Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

The time-varying autoregressive （TVAR） modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing , and the experiment result shows that the proposed method is effective in feature extraction.

“双支柱”政策调控与企业金融资产配置时变关系研究:基于TVP-SV-VAR模型的实证检验

金融科技发展和市场制度变革给传统金融体系带来的巨大冲击,使得“双支柱”政策调控的有效性面临严峻考验。借助TVP-SV-VAR模型,实证检验“双支柱”政策调控对企业金融资产配置的动态脉冲效应。研究表明,货币政策和宏观审慎政策的调控效果呈现出阶段性的演化特征,二者的协同效应能够有效抑制企业金融资产的过度配置。相比制度环境冲击,“双支柱”政策调控在面对金融环境冲击时表现出更强的作用效果和力度。进一步研究发现,“双支柱”政策通过资本流动管理、资产价格稳定和金融风险缓释途径影响企业金融资产配置,且对流动性与非流动性金融资产配置的作用效果存在显著差异。研究结论为理解实体企业金融化和完善金融市场制度提供了新的理论视角,也为政府相关部门依据金融环境和制度环境进行“双支柱”政策调控的动态调整提供决策依据。

Time-varying parameter auto-regressive models for autocovariance nonstationary time series

In this paper, autocovariance nonstationary time series is clearly defined on a family of time series. We propose three types of TVPAR (time-varying parameter auto-regressive) models: the full order TVPAR model, the time-unvarying order TVPAR model and the time-varying order TV-PAR model for autocovariance nonstationary time series. Related minimum AIC (Akaike information criterion) estimations are carried out.

Identifi cation of model structure parameters via combination of AFMM and ARX from seismic response data

To identify the model structure parameters in shaking table tests from seismic response, especially from time- varying response records, this paper presents a new methodology by combining the online recursive Adaptive Forgetting through Multiple Models （AFMM） and offtine Auto-Regression with eXogenous variables （ARX） model. First, the AFMM is employed to detect whether the response of model structure is time-invariant or time-varying when subjected to strong motions. Second, if the response is time-invariant, the modal parameters are identified from the entire response record, such as the acceleration time-history using the ARX model. If the response is time-varying, the acceleration record is divided into three segments according to the accurate time-varying points detected by AFMM, and parameters are identified by only using the tail segment data, which is time-invariant and suited for analysis by the ARX model. Finally, the changes in dynamic properties due to various strong motions are obtained using the presented methodology. The feasibility and advantages of the method are demonstrated by identifying the modal parameters of a 12-story reinforced concrete （RC） frame structure in a shaking table test.

Delay-dependent robust passivity control for uncertain time-delay systems

The robust passivity control problem is addressed for a class of uncertain delayed systems with timevarying delay. The parameter uncertainties are norm-bounded. First, the delay-dependent stability sufficient condition is obtained for the nominal system, and then, based-on the former, the delay-dependent robust passivity criteria is provided and the corresponding controller is designed in terms of linear matrix inequalities. Finally, a numerical example is given to demonstrate the validity of the proposed approach.

Novel l_2-l_∞ controller design for LPV discrete time-delay systems

One of the first attempts to derive energy-to-peak performance criteria and state-feedback controller design problem for linear parameter-varying discrete time systems with time delay is provided. Firstly, we present a parameter-dependent l 2-l ∞ performance criterion using a parameter-dependent Lyapunov function. Upon the conditions addressed, an improved parameter-dependent l 2-l ∞ performance criterion is established by the introduction of a slack variable, which exhibits a kind of decoupling between Lyapunov functions and system matrices. This kind of decoupling enables us to obtain more easily tractable conditions for analysis and synthesis problems. Then, the corresponding parameter-dependent state-feedback controller design is investigated upon these performance criteria, with sufficient conditions obtained for the existence of admissible controllers in terms of parameterized linear matrix inequalities. Finally, a numerical example is provided to illustrate the feasibility and advantage of the proposed controller design procedure.

基于渐消因子的ECEF-GLS估计算法

传统的误差配准算法假设系统偏差恒定或缓慢变化,当系统误差发生突变或快速变化时,这一假设不再成立。针对这一问题,研究了时变条件下的误差配准算法,引入渐消因子,对常规的基于地心地固坐标系的广义最小二乘算法(generalized least squares algorithm based on the earth-centered earth-fixed coordinate system,ECEF-GLS)进行了修正,弱化历史量测对配准的影响,并对渐消因子的选取问题进行了研究,给出了合理的设计方法。算法验证表明,基于渐消因子的ECEF-GLS估计算法能够对时变的系统偏差进行有效估计,精度满足配准要求。

Local Influence on the Error-Correction Variable in a Cointegrated System

The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.

铁矿石价格、国际航运与中国股票市场间的时变溢出效应——来自TVP-VAR-DY模型的证据

采用时频参数向量自回归(TVP-VAR)连通性方法,使用2017年1月4日至2023年3月6日的日度数据探讨铁矿石价格、国际航运与中国股票市场间的时变溢出效应。实证结果表明:国际航运是最强烈的溢出效应;中国股票市场是溢出效应的传导因素;铁矿石价格是主要的接收者。整个溢出效应是时变的,在危机时期溢出效应显著增强,在特殊时期溢出与接收关系会发生短暂的改变。

采用联合算法识别人-桥耦合系统时变模态参数

为更加准确地识别在役人行桥结构的时变模态参数,提出了一种基于联合方法的人行桥瞬时频率和时变阻尼比识别方法。首先,采用联合方法分别识别模态分量信号的瞬时频率和自由衰减振动信号的时频系数。然后,通过推导瞬时频率、时频系数模值和阻尼比三者之间的函数关系,估算获得人-桥耦合系统的时变阻尼比。最后,通过人-桥耦合数值算例和华威桥步行试验对联合方法进行了验证。结果表明:联合方法可以有效改善多种复杂因素下自由衰减振动信号提取结果不准确的问题,并且该方法识别的瞬时频率和时变阻尼比曲线可以清晰地表征行人空间位置变化对钢-混组合人行桥频率和阻尼比的变化趋势。

Identifying topologies and system parameters of uncertaintime-varying delayed complex networks

Node dynamics and network topologies play vital roles in determining the network features and network dynamical behaviors.Thus it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks.

A Novel PF-LSSVR-based Framework for Failure Prognosis of Nonlinear Systems with Time-varying Parameters

Particle filtering （PF） is being applied successfully in nonlinear and/or non-Gaussian system failure prognosis. However, for failure prediction of many complex systems whose dynamic state evolution models involve time-varying parameters, the tradi- tional PF-based prognosis framework will probably generate serious deviations in results since it implements prediction through iterative calculation using the state models. To address the problem, this paper develops a novel integrated PF-LSSVR frame- work based on PF and least squares support vector regression （LSSVR） for nonlinear system failure prognosis. This approach employs LSSVR for long-term observation series prediction and applies PF-based dual estimation to collaboratively estimate the values of system states and parameters of the corresponding future time instances. Meantime, the propagation of prediction un- certainty is emphatically taken into account. Therefore, PF-LSSVR avoids over-dependency on system state models in prediction phase. With a two-sided failure definition, the probability distribution of system remaining useful life （RUL） is accessed and the corresponding methods of calculating performance evaluation metrics are put forward. The PF-LSSVR framework is applied to a three-vessel water tank system failure prognosis and it has much higher prediction accuracy and confidence level than traditional PF-based framework.

移动质量-简支梁系统的参数辨识

给出了移动质量 -简支梁系统的动力学方程 ,并用基于Hamilton原理的结构动响应算法对方程进行数值求解。然后介绍了一种新的从自由响应数据识别时变参数结构系统的伪模态参数的方法 ,并将该方法引入用于研究移动质量 -简支梁系统的参数辩识问题。