New identification method for Hammerstein models based on approximate least absolute deviation

Disorder and peak noises or large disturbances can deteriorate the identification effects of Hammerstein non-linear models when using the least-square (LS) method. The least absolute deviation technique can be used to resolve this problem; however, its absolute value cannot meet the need of differentiability required by most algorithms. To improve robustness and resolve the non-differentiable problem, an approximate least absolute deviation (ALAD) objective function is established by introducing a deterministic function that exhibits the characteristics of absolute value under certain situations. A new identification method for Hammerstein models based on ALAD is thus developed in this paper. The basic idea of this method is to apply the stochastic approximation theory in the process of deriving the recursive equations. After identifying the parameter matrix of the Hammerstein model via the new algorithm, the product terms in the matrix are separated by calculating the average values. Finally, algorithm convergence is proven by applying the ordinary differential equation method. The proposed algorithm has a better robustness as compared to other LS methods, particularly when abnormal points exist in the measured data. Furthermore, the proposed algorithm is easier to apply and converges faster. The simulation results demonstrate the efficacy of the proposed algorithm.     

基于近似偏最小一乘的闭环系统辨识新方法

本文基于近似最小一乘准则和主成分分析,针对反馈通道模型阶次低于前向通道模型阶次且反馈通道不存在噪声的闭环系统,进行了近似偏最小一乘递推辨识算法的推导.为解决最小一乘准则函数不可微的问题,本文算法用确定性可导函数近似代替残差绝对值.近似偏最小一乘辨识算法可以克服基于最小二乘准则的辨识算法在受到满足(SαS)分布的尖峰噪声干扰时残差平方项过大的缺点,具有目标函数可导,计算简单的优点.同时,通过主成分分析去除数据向量各元素之间的线性相关,可以得出模型参数的唯一解.仿真实验表明,本文算法可以对反馈通道模型阶次低于前向通道模型阶次的闭环系统进行直接辨识,抑制了尖峰噪声对辨识结果的影响,具有优良的稳健性,可以更好地应用于闭环系统辨识.     

基于递推近似最小一乘的多传感器系统偏差稳健估计算法

对于多传感器多目标跟踪问题,系统偏差对航迹融合精度有较大影响,因此在信息融合系统中,首先要对各传感器的系统偏差进行估计,而在含错误关联和观测野值的复杂环境下,传统系统偏差估计方法的性能会严重退化.对此,提出一种具有递推形式的近似最小一乘稳健估计算法,以减少异常噪声对偏差估计的不利影响.使用平方根平滑逼近函数替代最小一乘法的目标函数,基于牛顿方向及其秩1修正推导出该方法的递推求解框架.基于条件数分析,证明所提出算法的数值稳定性好于Huber方法.通过两个仿真算例,将所提出算法与已有其他算法进行对比验证.仿真结果表明,在含错误关联和观测野值的条件下,所提出算法可以改善偏差估计精度,并且明显好于已有的其他算法.     

M-估计耦合双边滤波的正则化超分辨率重建

在正则化超分辨率重建框架下,基于M-估计理论和双边滤波思想,建立了一种鲁棒的超分辨率重建统一能量泛函。该能量泛函融合了M-估计的鲁棒性处理机制和双边滤波的双重异性加权机制,提高了算法的鲁棒性和边缘保持特性。鉴于采用最小二乘估计的CLS算法和采用最小一乘估计的Farsiu重建算法在边缘保持特性方面存在的不足,在算法实现时选用了Huber稳健M-估计。不论是视觉效果还是峰值信噪比(PSNR),实验结果都表明该算法的有效性。     

Adaptive recursive algorithm with logarithmic transformation for nonlinear system identification in α-stable noise

Although the least mean pth power (LMP) and normalized LMP (NLMP) algorithms of adaptive Volterra filters outperform the conventional least mean square (LMS) algorithm in the presence of α-stable noise, they still exhibit slow convergence and high steady-state kernel error in nonlinear system identification. To overcome these limitations, an enhanced recursive least mean pth power algorithm with logarithmic transformation (RLogLMP) is proposed in this paper. The proposed algorithm is adjusted to minimize the new cost function with the p-norm logarithmic transformation of the error signal. The logarithmic transformation, which can diminish the significance of outliers under α-stable noise environment, increases the robustness of the proposed algorithm and reduces the steady-state kernel error. Moreover, the proposed method improves the convergence rate by the enhanced recursive scheme. Finally, simulation results demonstrate that the proposed algorithm is superior to the LMP, NLMP, normalized least mean absolute deviation (NLMAD), recursive least squares (RLS) and nonlinear iteratively reweighted least squares (NIRLS) algorithms in terms of convergence rate and steady-state kernel error.     

基于CIM的偏差补偿稀疏NLMAD算法研究

为了解决输入信号受噪声干扰和输出观测噪声具有脉冲特征的稀疏系统辨识问题,提出一种基于CIM的偏差补偿NLMAD(Normalized least mean absolute deviation, NLMAD)算法。 利用NLMAD算法可有效抵御脉冲输出观测噪声的优势,首先应用无偏准则设计偏差补偿NLMAD算法来有效解决由于输入噪声导致的估计偏差问题。再次考虑到稀疏系统辨识问题,将CIM作为稀疏约束惩罚项引入到偏差补偿NLMAD算法提出了新的稀疏自适应滤波算法CIMBCNLMAD。将所提算法应用于输入和输出均含有噪声的稀疏系统辨识和回声干扰抵消场景中,实验表明CIMBCNLMAD算法的稳态性能优于其它自适应滤波算法,说明该方法具有强的鲁棒性且可应用于工程实践。     

基于最小一乘的系统辨识方法研究

针对系统参数辨识中最小二乘估计的稳健性较差,给出稳健性较强的最小一乘的系统辨识方法。推导出了最小一乘回归系数的估计式,使用逐次逼近迭代的方法,构造迭代序列给出最小一乘回归系数的迭代算法。并把该算法应用于控制系统参数辨识中,与最小二乘辨识相比较,当模型的观测数据有测量噪声时,最小一乘回归系数的收敛性及数值稳定性较好。仿真结果验证了理论,显示了最小一乘辨识的优越性。     

一种传感网时间同步和定位的联合线性估计方法

采用时间测量以估计节点位置的方法实现简单,在传感网中得到了广泛的使用。然而节点计时时钟存在漂移和偏离,导致时间测量不准确。为此文本以节点时钟漂移和偏离模型为基础,提出了一种时间同步和节点定位的联合线性估计方法,包括最小平方（LS）及权重最小平方（WLS）方法。仿真测试了所设计算法的运行时间,分析了噪声对联合估计方法的估计误差影响。结果表明,LS及WLS线性估计方法运算速度较半正定（SDP）算法快,在低噪声条件下LS及WLS线性估计方法具有较高的稳定性和定位精度。     

非最小相位系统的基函数型自适应迭代学习控制

针对重复运行的未知非最小相位系统的轨迹跟踪问题, 结合时域稳定逆特点, 提出了一种新的基函数型自适应迭代学习控制(Basis function based adaptive iterative learning control, BFAILC)算法. 该算法在迭代控制过程中应用自适应迭代学习辨识算法估计基函数模型, 采用伪逆型学习律逼近系统的稳定逆, 保证了迭代学习控制的收敛性和鲁棒性. 以傅里叶基函数为例, 通过在非最小相位系统上的控制仿真, 验证了算法的有效性.     

基于自适应重叠系数的T-S 模型在线辨识算法及应用

为使T-S模型在线辨识时能够更加合理地划分模糊空间,提出一种根据相邻聚类中心距离确定模糊空间重叠系数的方法.将该方法与一次完成最小二乘法、递推最小二乘法相结合,得到了一种辨识精度较高的T-S模型在线辨识算法.以某型号单晶炉热场的实际运行数据为对象,应用所提出的算法对热场模型进行在线辨识.辨识结果表明,由该辨识算法得到的单晶炉热场模型具有较高的精度.     

Data‐Driven Adaptive Critic Approach for Nonlinear Optimal Control via Least Squares Support Vector Machine

This paper develops an online adaptive critic algorithm based on policy iteration for partially unknown nonlinear optimal control with infinite horizon cost function. In the proposed method, only a critic network is established, which eliminates the action network, to simplify its architecture. The online least squares support vector machine (LS‐SVM) is utilized to approximate the gradient of the associated cost function in the critic network by updating the input‐output data. Additionally, a data buffer memory is added to alleviate computational load. Finally, the feasibility of the online learning algorithm is demonstrated in simulation on two example systems.     

近似最小一乘意义下的鲁棒卡尔曼滤波器

当存在高污染率的野值观测时,现有的鲁棒卡尔曼滤波器的数值稳定性和抗差能力可能会严重退化.为此,基于近似最小一乘估计和修正的高斯牛顿方法提出一种新的鲁棒卡尔曼滤波器,以减小含野量测对滤波器的不利影响.通过条件数分析和影响函数分析,从理论上证明所提出方法的数值稳定性和抗差能力均好于基于Huber估计的卡尔曼滤波器.通过仿真实验对理论分析结果进行验证.仿真结果表明,在只有少量野值观测的情况下,所提出的滤波器与Huber卡尔曼滤波器的估计性能大致相当;而在含有高污染率的野值观测时,所提出的滤波器的估计性能明显好于Huber卡尔曼滤波器.在仿真实验中还对几种滤波器的计算花费进行了比较,发现所提出滤波器的计算代价小于Huber卡尔曼滤波器的计算代价.     

基于SL0范数的改进稀疏信号重构算法

平滑范数（Smoothed l0,SL0）压缩感知重构算法通过引入平滑函数序列将求解最小l0范数问题转化为平滑 函数优化问题,可以有效地用于稀疏信号重构。针对平滑函数的选取和算法稳健性问题,提出一种新的平滑函数序列近似范数,结合梯度投影法优化求解,并进一步提出采用奇异值分解（Singular value decomposition, SVD）方法改进算法的稳健性,实现稀疏度信号的精确重构。仿真结果表明,在相同的测试条件下,本文算法相比OMP算法、SL0算法以及L1-magic算法在重构精度、峰值信噪比方面都有较大改善。     

An approach to polynomial NARX/NARMAX systems identification in a closed-loop with variable structure control

Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these approaches are for the open-loop identification. However, for reasons such as safety and production restrictions, open-loop identification cannot always be done. In such cases, closed-loop identification is necessary. This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control （VSC）. First, a genetic algorithm （GA） is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters. Second, the system is simulated again and its parameters are estimated by an algorithm of the least square （LS） family. Finally, simulation examples are given to show the validity of the proposed approach.     

Adaptive extended fuzzy basis function network

The structure of the extended fuzzy basis function network (EFBFN) is firstly proposed, and the least squares (LS) method is used to design it by fixing the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel evolutionary algorithm based on LS and the hybrid of evolutionary programming and particle swarm optimization (LS-EPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. The enhanced EFBFN whose parameters are refined automatically using LS-EPPSO is thus called adaptive EFBFN. In the simulation part, the proposed method to construct AEFBFN is employed to model a three input nonlinear function and to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed.     

MIMO-OFDM 系统角域LS 信道估计算法

当信道估计算法利用先验信息进行MIMO-OFDM 信道辨识时,计算复杂度将显著增加．针对这一问 题,设计一种无需先验信息且计算复杂度低的相移正交角域LS 算法．该算法的基本思想是：在保证LS 算法均方 误差（MSE）性能达到最小的前提下,根据角域内不同发送和接收天线间信道的空间独立特性,采用最有用抽头系 数（MST）技术来提高信道估计性能．仿真结果表明,所提算法具有良好的估计性能．     

一种基于滑模的自适应预测函数控制新算法

提出了一种应用带时变遗忘因子的基于滑模的自适应预测函数控制新算法。该算法采用带时变遗忘因子的递推最小二乘算法在线辨识模型参数,将滑模控制与预测函数控制(PFC)相结合对系统进行控制。与其他模型预测控制不同,预测函数控制可以克服其他模型预测控制可能出现规律不明的控制输入问题,具有良好的跟踪能力和较强的鲁棒性,离散滑模控制中的滑动模态对干扰具有不变性;最后分析了控制系统的闭环渐近稳定性。仿真结果验证了该方法的有效性。     

Material identification in structural optimization using response surfaces

This paper evaluates the use of a response surface optimization algorithm for structural material or parameter identification. The algorithm used is the successive response surface method (SRSM) as implemented in LS-OPT. Two methods are used in the formulation of the optimization problem. The first is to minimize the maximum deviation of the distance function between the simulated and experimental results at selected points, while the second approach minimizes the more standard least squares residual form of the distance function, effectively providing a compromised match over all the parameters selected. SRSM uses a trust region that is adapted using a heuristic contraction and panning approach. The method has only one user-required parameter, the size of the initial trust region. To illustrate the robustness of SRSM as a material identification tool, three test cases are presented. The first concerns the identification of the power-law material parameters of a simple tensile test specimen. The second test case determines the leakage coefficient-pressure load curve of an airbag given experimental kinematic data of a chest form impacting the airbag. In the third test case the material identification of a rate-dependent low-density foam material is conducted. It is shown that SRSM essentially converges within 10 iterations for all the test cases, and that the two distance function minimization approaches produce similar results.     

基于准最小二乘法的光伏阵列模型鲁棒参数估计方法

光伏阵列的模型参数估计在光伏发电系统的仿真、输出功率预测、最大功率点跟踪等方面有重要意义。当测量数据中只含随机误差时,以加权最小二乘（WLS）为优化函数的参数估计方法有较好的辩识效果。但是当测量数据中含有显著误差时,WLS参数辩识的效果较差。为解决此问题,本文提出了一种以准加权最小二乘法（QWLS）为优化函数的参数估计方法来减小显著误差的影响,采用了赤池信息量准则(AIC)设计QWLS最优参数,将该方法应用于光伏阵列中构造模型鲁棒参数估计问题。最后将WLS和QWLS分别结合序列二次规划（SQP）算法,进行光伏阵列模型的参数估计仿真与实验测试。测试结果显示QWLS参数估计结果更准确,验证了准最小二乘法的鲁棒性与有效性。     

A generalized least absolute deviation method for parameter estimation of autoregressive signals

This paper proposes a generalized least absolute deviation (GLAD) method for parameter estimation of autoregressive (AR) signals under non-Gaussian noise environments. The proposed GLAD method can improve the accuracy of the estimation of the conventional least absolute deviation (LAD) method by minimizing a new cost function with parameter variables and noise error variables. Compared with second- and high-order statistical methods, the proposed GLAD method can obtain robustly an optimal AR parameter estimation without requiring the measurement noise to be Gaussian. Moreover, the proposed GLAD method can be implemented by a cooperative neural network (NN) which is shown to converge globally to the optimal AR parameter estimation within a finite time. Simulation results show that the proposed GLAD method can obtain more accurate estimates than several well-known estimation methods in the presence of different noise distributions.