An adaptive actuator failure compensation scheme for a class of nonlinear MIMO systems

This paper develops an adaptive actuator failure compensation scheme for control of a class of nonlinear multi-input–multi-output systems with redundant actuators subject to uncertain failures. The design method is to estimate the failure pattern parameters and the failure signal parameters first, and then use the parameter estimates to construct the adaptive failure compensation controller, the control law calculation is done simultaneously with parameter estimation without explicit failure detection. Closed-loop signal boundedness and asymptotic output tracking, despite the actuator failure uncertainties, are ensured analytically and verified by simulation results from its application to attitude control of a near space vehicle dynamic model.     

基于四阶累积量的CSPRIT算法

分析了基于二阶统计的CSPRIT算法在空间相关高斯噪声环境中存在的问题，提出将四阶累积量与CSPRIT算法相结合，处理一维二元相移键控信号（BPSK）和多元幅移键控信号（MASK），实现信号到达角（DOA）的估计和波束形成器的构造。与基于二阶统计的CSPRIT算法相比，基于四阶累积量的改进算法能够有效抑制空间相关的高斯噪声，提高信号估计精度。计算机仿真验证了该算法的有效性。     

实时校正模型的抗差递推算法

利用遥测系统实时监测水情资料,由于遥测系统自身的原因以及水文要素测量的具体要求,数据常常携带异常误差。采用有异常误差的实测流量资料对实时校正模型进行参数辨识,要求算法既能抵御异常误差的影响,又具有较强的实时跟踪能力,以适应实时洪水预报的要求。在递推最小二乘算法的基础上,引入抗差理论,削弱异常值对参数估计的影响;引入遗忘因子,实时跟踪模型时变参数的变化。计算实例表明,带有遗忘因子的抗差递推最小二乘算法对异常误差不敏感,又具有较强的实时跟踪能力。     

一类广义隐马尔科夫模型的建模与参数估计

隐马尔科夫模型在很多方面已有广泛应用.讨论了一类更为一般的模型,这类模型由Wojciech Pieczynski首次提出,并且给出了在图像识别中的应用.这里首次给出在离散观测和离散状态下该模型的精确数学描述,其中包括建模、状态估计和参数估计,这些算法都是首次被提出的.     

Joint time-frequency synchronization and channel estimation in two-way relay networks

This work investigates joint time-frequency synchronization and channel estimation in two-way relay networks (TWRN) that utilize amplify-and-forward (AF) relay strategy. A two-way transmission model for joint time-frequency synchronization and channel estimation in TWRN is provided. Due to the high dimension and complexity of the model, the maximum likelihood (ML) algorithm is over complex and a standard Markov chain Monte Carlo (MCMC) algorithm is ineffective. We instead develop a hybrid Metropolis–Hastings–Gibbs algorithm in order to facilitate joint time-frequency recovery and effective channel estimation. In particular, we present a reparameterized model to facilitate Gibbs sampling with respect to the self-interference channel. Then the second order truncated Taylor series approximation is adopted for carrier frequency offsets (CFOs) estimation. Furthermore, a heuristic way of determining update order of parameters is proposed and initial value of the Markov chain is discussed as well. To test the robustness and objectivity of our proposed algorithm, two MCMC estimators, each with its own prior distribution, are compared against each other. Numerical results are provided to verify the effectiveness and robustness of proposed algorithm in terms of both mean-square errors (MSE) and estimation bias. The bit error rate (BER) performance results are further offered for comparison.     

一种新的双端通话检测方法研究

为了提升VOIP的通话质量,提高回声抵消系统中双端通话检测的准确性,本文提出一种新的双门限双端通话检测算法。新算法首先利用小波阈值去噪算法对接收的各信号进行去噪处理;然后对去噪处理后的小波子带信号进行动态能量阈值比较,识别出静音帧、活跃帧和发声帧;再对活跃帧与静音帧进行基音周期比较,以判断活跃帧是处在近端发声阶段,还是回声路径的变化。仿真实验证明,新的双门限检测算法表现良好。     

基于改进小波脊提取算法的数字信号瞬时频率估计方法

在深入分析小波脊原理的基础上,针对数字信号瞬时频率提取精度要求,利用小波脊提取数字信号的瞬时频率,并对方法的初始值设置、解析小波参数设置和估计精度设置等关键问题进行了分析并改进.实际信号实验表明,改进算法鲁棒性明显增强,实用价值较大.     

A stochastic multi-parameters divergence method for online auto-tuning of fractional order PID controllers

This paper presents a stochastic multi-parameters divergence method for online parameter optimization of fractional-order proportional–integral–derivative (PID) controllers. The method is used for auto-tuning without the need for exact mathematical plant model and it is applicable to diverse plant transfer functions. The proposed controller tuning algorithm is capable of adaptively responding to parameter fluctuations and model uncertainties in real systems. Adaptation skill enhances controller performance for real-time applications. Simulations and experimental observations are carried on a prototype helicopter model to confirm the performance improvements obtained by the online auto-tuning of fractional-order PID structure in laboratory conditions.     

A family of variable step-size sparsity-aware SSAF algorithms with individual-weighting-factors under model-driven method

Recently, the sparsity-aware sign subband adaptive filter algorithm with individual-weighting-factors (S-IWF-SSAF) was devised. To accomplish performance enhancement, the variable parameter S-IWF-SSAF (VP-S-IWF-SSAF) algorithm was developed through optimizing the step-size and penalty factor, respectively. Different from the optimization scheme, we devise a family of variable step-size strategy S-IWF-SSAF (VSS-S-IWF-SSAF) algorithms based on the transient model of algorithms via minimizing the mean-square deviation (MSD) on each iteration with some reasonable and frequently adopted assumptions and Price's theorem. And in order to enhance the tracking capability, an effective reset mechanism is also incorporated into the proposed algorithms. It is worth mentioning that the presented algorithms could acquire lower computational requirements and exhibit higher steady-state estimation accuracy obviously and acceptable tracking characteristic in comparison to the VP-S-IWF-SSAF algorithm. In addition, the stable step-size range in the mean and mean square sense and steady-state performance are concluded. And the computational requirements are exhibited as well. Monte-Carlo simulations for system identification and adaptive echo cancellation applications certify the proposed algorithms acquire superior performance in contrast to other related algorithms within various system inputs under impulsive interference environments.     

Robust identification of Wiener time-delay system with expectation-maximization algorithm

This paper considers the parameter estimation for Wiener time-delay systems with the output data contaminated with outliers. The time-delay and corrupted output data bring great challenges to the parameter estimation problem. The statistical model of the estimation problem is constructed based on the Laplace distribution and the identification problem is formulated in the scheme of the expectation-maximization (EM) algorithm. The negative effect of outliers imposed on the parameter estimation problem is sufficiently suppressed and the unknown time-delay and model parameters can be estimated simultaneously. The simulation example is given to demonstrate the effectiveness of the proposed algorithm.     

Parameter estimation for a class of radial basis function-based nonlinear time-series models with moving average noises

This paper focuses on the parameter estimation for radial basis function-based state-dependent autoregressive models with moving average noises (RBF-ARMA models). An extended projection algorithm is derived based on the negative gradient search. In order to reduce the sensitivity of the algorithm to noise and reduce the fluctuations of the parameter estimation errors, a modified extended stochastic gradient algorithm is proposed. By introducing a moving data window, a modified moving data window-based extended stochastic gradient algorithm is further developed to improve the parameter estimation accuracy. The simulation results show that the proposed algorithms can effectively estimate the parameters of the RBF-ARMA models.     

An LMS adaptive algorithm with a new step-size control equation

In this paper, we introduce a new variable step-size LMS (VSSLMS) adaptive algorithm. The algorithm step-size equations estimate an optimal derived step-size and are controlled by only one parameter. Mean-square performance analysis is provided for zero-mean stationary Gaussian input signal, and a simple expression that predicts the algorithm steady state misadjustment is derived for small step-size fluctuations. The algorithm is compared with other well-known VSSLMS algorithms through simulation experiments, which demonstrate the performance advantages of the proposed algorithm over these algorithms.     

Gradient-based iterative identification method for multivariate equation-error autoregressive moving average systems using the decomposition technique

This paper studies the parameter estimation problems of multivariate equation-error autoregressive moving average systems. Firstly, a gradient-based iterative algorithm is presented as a comparison. In order to improve the computational efficiency and the parameter estimation accuracy, a decomposition-based gradient iterative algorithm is presented by using the decomposition technique. The key is to transform an original system into two subsystems and to estimate the parameters of each subsystem, respectively. Compared with the gradient-based iterative algorithm, the decomposition-based algorithm requires less computational efforts, and the simulation results indicate that this algorithm is effective.     

社区服务中公众参与对公众满意的影响研究

建立了社区服务中公众参与影响公众满意的概念模型,提出相关假设,并进行相关量表设计。最后运用结构方程模型及LISREL参数估计进行模型验证,结果表明公众参与通过感知服务质量影响公众满意度。     

Decentralized adaptive synchronization with bounded identification errors for discrete-time nonlinear multi-agent systems with unknown parameters and unknown high-frequency gains

This paper addresses the challenging problem of decentralized adaptive control for a class of coupled hidden leader-follower multi-agent systems, in which each agent is described by a nonlinearly parameterized uncertain model in discrete time and can interact with its neighbors via the history information from its neighbors. One of the agents is a leader, who knows the desired reference trajectory, while other agents cannot receive the desired reference signal or are unaware of existence of the leader. In order to tackle unknown internal parameters and unknown high-frequency gains, a projection-type parameter estimation algorithm is proposed. Based on the certainty equivalence principle and neighborhood history information, the decentralized adaptive control is designed, under which, the boundedness of identification error is guaranteed with the help of the Lyapunov theory. Under some conditions, it is shown that the multi-agent system eventually achieves synchronization in the presence of strong couplings. Finally, a simulation example is given to support the results of the proposed scheme.     

基于DTMB的无源雷达在车辆定位中的应用

无源雷达系统是基于机会照射源对目标进行定位、跟踪甚至识别的系统,这种雷达系统具有先进的系统架构与算法,应用前景十分广泛。本文提出一种基于地面数字电视广播信号的无源雷达用于车辆定位与跟踪的技术。首先分析DTMB （Digital Television Terrestrial Multimedia Broadcasting）信号作为非合作照射源的优势,提出了基于DTMB信号的无源雷达目标定位与跟踪的算法,然后通过构建试验平台,实地测量数据,利用MATLAB设计实时数据处理算法对移动车辆的运动状态进行定位与跟踪。实验结果表明,利用无源雷达进行移动车辆的定位与跟踪是完全可行的。     

Broad echo state network for multivariate time series prediction

In this paper, a broad echo state network with multiple reservoirs in parallel configuration (Broad-ESN) is proposed for a class of multivariate time series prediction. Firstly, through the unsupervised learning algorithm of restricted Boltzmann machine (RBM), the number of reservoirs of Broad-ESN can be determined, such that the dynamic characteristics of a class of multivariate time series can be fully reflected. Secondly, a parameter optimization method based on Davidon–Fletcher–Powell (DFP) quasi-Newton algorithm is proposed to optimize the reservoir parameters of Broad-ESN. Meanwhile, an output weights learning method based on output error is given to train the output weights of Broad-ESN. Thirdly, a sufficient condition for the echo state property of Broad-ESN is given. Finally, four examples are given to verify the effectiveness of Broad-ESN.     

Identification of Gaussian process with switching noise mode and missing data

In traditional system identification methods, it is often assumed that the output data are corrupted by Gaussian white noise which is independent and identically distributed (i.i.d.). However, this assumption may lead to poor robustness since the noise characteristic often varies throughout the sampling process. In this work, output measurements affected by switching Gaussian noise are considered. In addition, a Markov chain model is utilized to describe the multi-mode behavior of the noises. Meanwhile, the collected data are usually incomplete in practice. Taking these circumstances into account, a new algorithm for Gaussian process regression (GPR) with switching noise mode and missing data is introduced. The parameters of the model are estimated by expectation maximization (EM) algorithm via conjugate gradient (CG) method. Two numerical examples along with a continuous stirred tank reactor simulation are employed to verify the effectiveness of the proposed algorithm. The superior performance is demonstrated by comparing the proposed algorithm with other existing relevant methods.     

Echo state network with multiple delayed outputs for multiple delayed time series prediction

In this paper, considering the influence of multiple delayed output items on the prediction accuracy of echo state network, a novel echo state network with multiple delayed outputs (MDO-ESN) is proposed for time series prediction with multiple delayed outputs. Firstly, for a given learning task, through studying the autocorrelation of output signal, its delayed characteristics can be determined, and then the corresponding delayed item of output equation of the MDO-ESN can be adjusted adaptively. Secondly, in order to improve the adaptability of the MDO-ESN in different learning tasks, a sufficient condition is given to satisfy the stability of the MDO-ESN. Thirdly, a parameter optimization method is given to reduce the dependence of the prediction accuracy of the MDO-ESN on the reservoir parameters of the MDO-ESN. Finally, two numerical simulation examples and one actual simulation example are used for verifying the effectiveness of the MDO-ESN.     

Optimized search over the Gabor dictionary for note decomposition and recognition

Performing real-time note detection of multiple audio sources, such as guitar signals, is a difficult task due to the complex nature of these signals. For instance, guitar notes are close in pitch and the harmonic overtones of each note are strongly interlaced, thus preventing standard filtering techniques. In order to accomplish note detection, a harmonic matching pursuit algorithm has been used to decompose an audio signal in terms of elementary waveforms called harmonic atoms. These atoms are derived from the standard matching pursuit algorithm, and are part of an extended and overcomplete Gabor dictionary. In this paper, the search over Gabor dictionary is optimized by using signal modeling of the guitar signal in order to pre-calculate a parameter set; therefore avoiding a costly search over the extended Gabor dictionary. The parameter set defined for the proposed algorithm includes time-location, decay rate, frequency, scale and phase, which are calculated at the onset of each note played. This optimized algorithm is demonstrated through synthesized and real guitar signal examples. Considerable computational savings of this proposed algorithm over the harmonic matching pursuit algorithm are achieved.