Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

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.

Adaptive Containment Control for Fractional-Order Nonlinear Multi-Agent Systems With Time-Varying Parameters

This paper investigates adaptive containment control for a class of fractional-order multi-agent systems(FOMASs)with time-varying parameters and disturbances.By using the bounded estimation method,the difficulty generated by the timevarying parameters and disturbances is overcome.The command filter is introduced to solve the complexity problem inherent in adaptive backstepping control.Meanwhile,in order to eliminate the effect of filter errors,a novel distributed error compensating scheme is constructed,in which only the local information from the neighbor agents is utilized.Then,a distributed adaptive containment control scheme for FOMASs is developed based on backstepping to guarantee that the outputs of all the followers are steered to the convex hull spanned by the leaders.Based on the extension of Barbalat's lemma to fractional-order integrals,it can be proven that the containment errors and the compensating signals have asymptotic convergence.Finally,three simulation examples are given to show the feasibility and effectiveness of the proposed control method.

Genome-wide Association Analysis of Fast Chlorophyll Fluorescence Parameters in Maize

Fast chlorophyll fluorescence parameters are widely used to characterize the photosynthetic efficiency of plants. In this study, a genome-wide association analysis was used to detect key single-nueleotide polymorphisms （SNPs） associated with fast chlorophyll fluorescence parameters using more than 560 000 SNPs in a maize panel consisting of 404 inbred lines. In four fidd environments, 41 SNPs were detected to be associated with five fast chlorophyll fluorescence parameters, including ABS/CS0, ET0/CS0, TR0/ABS, ET0/TR0 and Pies. Among these identified SNPs, 8, 6, 18, 4 and 5 were significantly associated with ET0/TR0, ABS/ CS0, TR0/ABS, ET0/CS, and Plcs, respectively. These SNPs will help to discover genes for chlorophyll fluorescence parameters, better understand the genetic basis of photosynthesis, and assist in developing marker-assisted selection breeding programs in maize.

Effect of material parameters on stress wave propagation during fast upsetting

Based on a dynamic analysis method and an explicit algorithm,a dynamic explicit finite element code was developed for modeling the fast upsetting process of block under drop hammer impact,in which the hammer velocity during the deformation was calculated by energy conservation law according to the operating principle of hammer equipment.The stress wave propagation and its effect on the deformation were analyzed by the stress and strain distributions.Industrial pure lead,oxygen-free high-conductivity (OFHC) copper and 7039 aluminum alloy were chosen to investigate the effect of material parameters on the stress wave propagation.The results show that the stress wave propagates from top to bottom of block,and then reflects back when it reaches the bottom surface.After that,stress wave propagates and reflects repeatedly between the upper surface and bottom surface.The stress wave propagation has a significant effect on the deformation at the initial stage,and then becomes weak at the middle-final stage.When the ratio of elastic modulus or the slope of stress-strain curve to mass density becomes larger,the velocity of stress wave propagation increases,and the influence of stress wave on the deformation becomes small.

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.

Optimal Parameter Estimation of Transmission Line Using Chaotic Initialized Time-Varying PSO Algorithm

Transmission line is a vital part of the power system that connects two major points,the generation,and the distribution.For an efficient design,stable control,and steady operation of the power system,adequate knowledge of the transmission line parameters resistance,inductance,capacitance,and conductance is of great importance.These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable.This paper presents a method to optimally estimate the parameters using the input-output quantities i.e.,voltages,currents,and power factor of the transmission line.The equivalentπ-network model is used and the terminal data i.e.,sending-end and receiving-end quantities are assumed as available measured data.The parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective function.An improved particle swarm optimization(PSO)in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line parameters.Two cases are considered for parameter estimation,the first case is when the line conductance is neglected and in the second case,the conductance is considered into account.The results obtained by the improved algorithm are compared with the standard version of the algorithm,firefly algorithm and artificial bee colony algorithm for 30 number of trials.It is concluded that the improved algorithm is tremendously sufficient in estimating the line parameters in both cases validated by low error values and statistical analysis,comparatively.

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.

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.

Prestack seismic stochastic inversion based on statistical characteristic parameters

In the conventional stochastic inversion method,the spatial structure information of underground strata is usually characterized by variograms.However,effectively characterizing the heterogeneity of complex strata is difficult.In this paper,multiple parameters are used to fully explore the underground formation information in the known seismic reflection and well log data.The spatial structure characteristics of complex underground reservoirs are described more comprehensively using multiple statistical characteristic parameters.We propose a prestack seismic stochastic inversion method based on prior information on statistical characteristic parameters.According to the random medium theory,this method obtains several statistical characteristic parameters from known seismic and logging data,constructs a prior information model that meets the spatial structure characteristics of the underground strata,and integrates multiparameter constraints into the likelihood function to construct the objective function.The very fast quantum annealing algorithm is used to optimize and update the objective function to obtain the fi nal inversion result.The model test shows that compared with the traditional prior information model construction method,the prior information model based on multiple parameters in this paper contains more detailed stratigraphic information,which can better describe complex underground reservoirs.A real data analysis shows that the stochastic inversion method proposed in this paper can effectively predict the geophysical characteristics of complex underground reservoirs and has a high resolution.

TV/L2-based image denoisingalgorithm with automaticparameter selection

In order to improve the adaptiveness of TV/L2-based image denoising algorithm in differ- ent signal-to-noise ratio （SNR） environments, an iterative denoising method with automatic parame- ter selection is proposed. Based upon the close connection between optimization function of denois- ing problem and regularization parameter, an updating model is built to select the regularized param- eter. Both the parameter and the objective function are dynamically updated in alternating minimiza- tion iterations, consequently, it can make the algorithm work in different SNR environments. Mean- while, a strategy for choosing the initial regularization parameter is presented. Considering Morozov discrepancy principle, a convex function with respect to the regularization parameter is modeled. Via the optimization method, it is easy and fast to find the convergence value of parameter, which is suitable for the iterative image denoising algorithm. Comparing with several state-of-the-art algo- rithms, many experiments confirm that the denoising algorithm with the proposed parameter selec- tion is highly effective to evaluate peak signal-to-noise ratio （PSNR） and structural similarity

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.

Off-Line Identification of Induction Motor Parameters

This study presented an off-line identification method of induction motor (IM) parameters. Before startup,the inverter drive performed automatically a modified DC test, a locked-rotor test, a no-load test and a step-voltage test to identify all the parameters of an induction motor. No manual operation and speed signals were required in the process. In order to obtain effective messages and improve the accuracy of identification, the discrete fast Fourier transform (DFFT) and the least-squares were used to process the signals of currents and voltages. A phase-voltage measuring method for motors was also proposed, which measured directly the actual conducting time of three upper switches in the inverter without need for a dead-time compensator. The validity, reliability and accuracy of the presented methods have been verified by the experiments on a VSI-fed IM drive system.

Parameter Estimation Approach of FSK/PSK Radar Signal

A parameter estimation approach of reconnaissance hybrid radar signal combined frequency-shift keying （FSK） and phase-shift keying （PSK） is presented. Firstly, the multi-phase difference is adopted to calculate the instantaneous frequency （IF） of FSK/PSK, then the frequency points of FSK are estimated from the histogram of IF. The code rate of PSK is extracted from the locations of phase discontinuity. Finally, the multi-phase difference of the square of the received signal is computed to estimate the code rate of FSK. The presented algorithm has higher accuracy of parameter estimation when the signal-to-noise ratio （SNR） is above 11 dB.

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.

FMLSMETHODOFPARAMETERESTIMATIONANDITSAPPLICATIONTOHEAT┐EXCHANGERIDENTIFICATION

Using the inversion of the auto correlation function Toeplitz matrix of pseudo random binary sequence (PRBS) derived in this paper and the theorem of partitioned matrix inversion, a fast multistage least squares (FMLS) method is developed. Its performances are theoretically analyzed and digital simulation is made to compare FMLS with multistage least squares (MSLS), correlation least squares(COR LS) and LS for their computer speed and identification accuracy. Finally, FMLS is applied to identifying the heat excharger dynamics. It is shown that FMLS is a good and effective identification technique.

Correlation of Commonly Used Anthropometric Parameters with FPG,FPG-CV and HbA1C

Objective:To investigate the relationship between body mass index(BMI),waist circumference(WC),hip circumference(HC),waist-hip ratio(WHR),waist height ratio(WHtR),fasting blood glucose(FPG),fasting blood glucose variation(FPG-CV)and glycosylated hemoglobin(HbA1C)to provide a reference for predicting the risk and development trend of diabetes mellitus.Methods:From October 2016 to December 2017,111 subjects from the Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine were selected to measure their height and weight.BMI,WHR and WHtR were calculated and the concentrations of FPG and HbA1C were detected.Statistical analysis was carried out with SPSS21.0 software,and the data were processed by multiple linear regression analysis.Results:The concentration dependence of WHtR,FPG,FPG-CV and HbA1C were more closely related.There was a significant difference between WHtR and FPG,FPG-CV and HbA1C by multiple linear regression analysis(t=8.531,6.910 and 6.905,respectively,P<0.01),and the correlation coefficient was 0.633,0.552 and 0.552 respectively(P<0.01).Conclusion:There is a significant correlation between WHtR and FPG,FPG-CV and HbA1C.Therefore,measuring the height and waist circumference for the predictive potential of diabetes needs to be emphasized and intensified.

巨型射电望远镜(FAST)反射面支承结构参数敏感性分析

研究分析500m口径球冠状巨型射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope,简称FAST)反射面支承结构参数敏感性问题。首先对目前所有敏感性分析方法进行综述,详细探讨基于回归和相关的参数全局敏感性分析方法,由于大规模结构输入参数(成千甚至上万)仍然是限制目前所有敏感性分析方法应用的瓶颈,提出具体预处理措施,在满足精度的基础上又使得计算变得可行。基于改进后的方法,科学合理地设定参数概率分布,系统地分析FAST反射面支承结构参数对其使用性能指标——反射面形状拟合精度RMS和安全性能指标——索网最大应力的敏感性问题,并统计分析、比较不同类型参数的总敏感性影响,结果指出,在所有结构参数中,索截面面积的不确定性对结构影响最大,其对RMS不确定性的相对贡献率占到了59%,为FAST反射面结构参数优化设计、施工质量的控制等提供有价值的信息,是指导FAST建造的参考依据。

基于Fast-ICA算法的改进EEMD算法在桥梁工程中的运用

目前,以振动分析为基础的桥梁结构健康状态评估方法逐渐受到人们的重视,模态参数识别作为振动分析的关键问题之一,还需进一步完善。模态参数识别即是对结构的振动信号进行动力特性参数的识别,以得到结构的频率、振型以及阻尼比。为了精确地识别出桥梁结构的模态参数,需先对传感器采集的结构响应信号进行分解和重构以保留结构的真实信息,再对重构信号进行模态参数辨识。现阶段,集合经验模态分解算法作为常用的信号分解算法之一,存在两大缺陷,即所得本征模态函数间存在模态混叠现象和有效本征模态函数的定义没有统一的标准。基于此提出对应的改进算法,首先将夹角余弦法用于筛选有效IMF分量;其次将盲源分离算法中的Fast-ICA算法嵌入EEMD分解算法中以避免模态混叠现象的发生;接着将所提算法运用于模拟信号以验证其可行性;最后将所提改进算法运用于实际桥梁工程的响应信号中,并对比分析所得模态参数值以验证该算法的可靠性。结果表明,所提算法较现有EEMD算法具有更好的信号分解效率,且能够有效地剔除结构的虚假信息,保留实际桥梁结构的真实信息。

基于历史连接信息改进的单边加速FASTTCP算法

针对FAST TCP存在难以选择协议参数的公开问题,根据FASTTCP单边加速应用的特点,提出了一种改进的2层结构的传输层算法。用此算法时,在下层,记录各活跃的FAST TCP连接小时间尺度收集最大排队延时等历史信息,上层算法充分利用历史连接能够提供的最大排队延时等信息,确定目标排队延时的控制范围,大时间尺度周期动态调整协议参数,通知给下层运行的FAST TCP连接,主动控制瓶颈链路的队列长度,避免队列溢出。NS-2仿真实例证明,该改进算法可大幅提高系统的稳定性和利用率。