时滞和滤波联合辨识的递推最小二乘算法

时滞和滤波联合辨识问题既是经典时滞估计问题的推广，又是自适应系统建模和时灌估计两方面的交叉。本文针对先滤波后时滞的系统模型，从方法上改进BOUDREAU＆KABAL提出的基于快速横向滤波器的递谁最小二乘滤波算法，使其时间复杂度由O［19ρ］下降为0［7ρ］，便于实时在线应用。我们以低通滤波器与线性时滞串联系统的辨识为例，表现该算法对变化时滞的跟踪能力及联合辨识性能。     

具有Hammerstein模型特征的非线性远程预报辨识

为提高内河船舶远程通信预警功能的精度和可靠性,提出了具有非线性预测控制模型特征的远程复杂目标预报辨识算法。通过将远程复杂目标的辨识分解为一个静态非线性环节和动态线性环节的串联,利用稳态信息获取稳态模型的一致性估计,并通过动态模型获得非线性静态环节的增益,再利用奇异值分解法和动态信息辨识获取非线性系统未知参数的估计。仿真结果验证了该方法的有效性。     

带遗忘因子的限定记忆辨识算法

基于最小二乘辨识原理,针对快时变系统,提出了一种将遗忘因子算法与限定记忆算法相结合的辨识算法,该方法在固定记忆长度中加入遗忘因子,并根据估计参数的变化率来在线调节遗忘因子的大小,提高了时变系统的辨识精度,数值仿真结果表明该方法有较好的辨识效果。     

非凸投影自适应Hammerstein滤波算法

该文研究了Hammerstein系统参数辨识和非线性系统预测问题,提出一种基于非凸投影的自适应滤波算法。论文将问题归结为具有非凸可行域的约束优化问题,并建立了基于交替方向乘子法(ADMM)和递归最小二乘相结合的算法框架。在该算法框架下,非凸约束优化问题的全局最优解可通过岭回归和欧几里得(Euclid)投影循环计算得到。将提出的算法分别应用于Hammerstein系统的参数辨识、非线性未知系统预测以及非线性声学回声消除,并进行仿真实验,结果显示所提算法具有较好的收敛性和稳定性,能够得到较准确的辨识和预测效果。     

超级电容模型的递推增广最小二乘辨识

为了更好的描述超级电容的电气特性,需要建立超级电容模型。提出系统辨识方法进行建模,阐述了系统辨识的原理和递推增广最小二乘法算法,在此基础上利用MATLAB编写递推增广最小二乘法程序估计出超级电容的传递函数,通过仿真对辨识结果进行验证。可知,该超级电容模型是正确有效的。     

基于递推最小二乘算法的小信号检测

受到强干扰影响的小信号通常难于有效检测。在分析递推最小二乘算法(RLS)原理及其几种改进形式的基础上,采用自适应方法将已检测出的大信号与原混叠信号对消,降低大信号对小信号的遮蔽作用,再进行小信号的检测。最后通过仿真证明,该方法能够在较小失真的情况下,有效检测出被大调幅信号干扰下的小调频信号;同时分别比较了各种算法的优劣,得出基于可变遗忘因子的RLS(VFF-RLS)算法不仅具有较快的收敛速度,而且收敛之后具有很好的平稳性能。     

FIR数字滤波器的递推最小二乘设计算法

本文考虑对称系数及反对称系数的ＦＩＲ数字滤波器的设计问题,设计准则选为最小加权平方误差准则,并将这个设计问题看成一个线性系统的辨识问题,辨识系统参数所需的输入数据由一随机抽样法产生,辨识算法采用递推最小二乘法．按随机抽样法产生的数据具有很强的激励,保证了被辨识参数的收敛性,同时又自然地实现了最小加权平方误差准则．两个设计范例说明了本文提出的设计方法的有效性．     

基于可变遗忘因子RLS的Boost转换器在线多参数辨识北大核心CSCD

针对Boost转换器控制性能受电感和电容变化影响的问题,提出了一种基于可变遗忘因子递推最小二乘法(recursive least squares method,RLS)的在线多参数辨识算法.考虑电感电流纹波,推导了精确的电感和电容辨识模型.在此基础上,研究了RLS算法中遗忘因子动态取值问题.通过在算法的误差信号中恢复系统噪声的方法,动态计算遗忘因子的取值,解决了传统RLS算法难以兼顾稳态精度和参数跟踪能力的问题.仿真结果表明,该算法可以在动态条件下,精确且快速地跟踪电感和电容值的变化,且具有良好的鲁棒性.     

基于递推阻尼最小二乘法的电力系统频率跟踪

最小二乘法是一种广泛的系统辨识和参数辨识方法,适于离线辨识,在线辨识参数时有其局限性。基于传统的最小二乘法,提出一种带有遗忘因子的有限数据窗口和自适应阻尼因子的递推阻尼最小二乘参数辨识算法。针对电力系统信号为理想信号、含有谐波及噪声和电力系统频率瞬间发生跳变的情况,分别做了仿真。仿真结果验证了该方法的鲁棒性、快速性和准确性,且算法效果优于其他方法。     

Blind identification of a class of nonlinear systems with cyclostationary input

This letter deals with blind identification of nonlinear discrete Hammerstein system under the input signal that is cyclostationary. The first-order moment of the specific input as well as the inverse nonlinear mapping of the Hammerstein model are combined to establish a relationship between the system output and the system parameters, which implies an approach to identifying the system blindly. Simulation results demonstrate the effectiveness of this approach to blind identification of a class of nonlinear systems.     

A Multi-innovation Recursive Least Squares Algorithm with a Forgetting Factor for Hammerstein CAR Systems with Backlash

This study addresses the identification of Hammerstein CAR systems with backlash, where the nonlinear backlash is described as one regression identification model using a two switching function mathematical model. In such a case, the Hammerstein CAR systems with backlash can be transformed into a piecewise linearized model. Then, a novel multi-innovation recursive least squares algorithm with a forgetting factor is applied to estimate the parameters of the proposed model. Finally, numerical examples are presented to test the performance of the proposed algorithm.     

Extended Kernel Recursive Least Squares Algorithm

This paper presents a kernelized version of the extended recursive least squares (EX-KRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS), or equivalently a general nonlinear state model in the input space. The center piece of this development is a reformulation of the well known extended recursive least squares (EX-RLS) algorithm in RKHS which only requires inner product operations between input vectors, thus enabling the application of the kernel property (commonly known as the kernel trick). The first part of the paper presents a set of theorems that shows the generality of the approach. The EX-KRLS is preferable to 1) a standard kernel recursive least squares (KRLS) in applications that require tracking the state-vector of general linear state-space models in the kernel space, or 2) an EX-RLS when the application requires a nonlinear observation and state models. The second part of the paper compares the EX-KRLS in nonlinear Rayleigh multipath channel tracking and in Lorenz system modeling problem. We show that the proposed algorithm is able to outperform the standard KRLS and EX-RLS in both simulations.     

一种基于拟牛顿法的递归最小二乘方法

基于拟牛顿优化方法,提出了一种稳健的自适应FIR滤波算法。新算法用最小二乘误差(LSE)代替了均方误差(MSE)作为代价函数,它具有和常规递归最小二乘(CRLS)算法相近似的追踪能力,且不存在数值计算不稳定性的问题,在收敛速度以及稳态效果方面也要优于De Campos的拟牛顿(QN)算法。通过计算机仿真比较了有关算法的性能。     

Numeric Variable Forgetting Factor RLS Algorithm for Second-Order Volterra Filtering

Nonlinear adaptive filtering techniques for system identification (based on the Volterra model) are widely used for the identification of nonlinearities in many applications. In this correspondence, the improved tracking capability of a numeric variable forgetting factor recursive least squares (NVFF-RLS) algorithm is presented for first-order and second-order time-varying Volterra systems under a nonstationary environment. The nonlinear system tracking problem is converted into a state estimation problem of the time-variant system. The time-varying Volterra kernels are governed by the first-order Gauss–Markov stochastic difference equation, upon which the state-space representation of this system is built. In comparison to the conventional fixed forgetting factor recursive least squares algorithm, the NVFF-RLS algorithm provides better channel estimation as well as channel tracking performance in terms of the minimum mean square error (MMSE) for first-order and second-order Volterra systems. The NVFF-RLS algorithm is adapted to the time-varying signals by using the updating prediction error criterion, which accounts for the nonstationarity of the signal. The demonstrated simulation results manifest that the proposed method has good adaptability in the time-varying environment, and it also reduces the computational complexity.     

Least Squares Identification for Hammerstein Multi-input Multi-output Systems Based on the Key-Term Separation Technique

System modeling and parameter estimation are basic for system analysis and controller design. This paper considers the parameter identification problem of a Hammerstein multi-input multi-output (H-MIMO) system. In order to avoid the product terms in the identification model, we derive a pseudo-linear identification model of the H-MIMO system through separating a key term from the output equation of the system and present a hierarchical generalized least squares (LS) algorithm for estimating the parameters of the system. Moreover, we present a new LS algorithm to reduce the computational burden. The proposed algorithms are simple in principle and can achieve a higher computational efficiency than the over-parameterization-based LS estimation algorithm. Finally, we test the proposed algorithms by the simulation example and show their effectiveness.     

用于FIR滤波器的递归最小二乘拟牛顿算法

基于拟牛顿优化方法,提出了一种稳健的自适应FIR滤波算法。新算法用最小二乘误差(LSE)代替了均方误差(MSE)作为代价函数,他具有和常规递归最小二乘(CRLS)算法相近似的追踪能力,且不存在数值计算不稳定性的问题,在收敛速度以及稳态效果方面也要优于DeCampos的拟牛顿(QN)算法。由计算机仿真比较了有关算法的性能。     

Maximum Likelihood Recursive Least Squares Estimation for Multivariable Systems

This paper discusses parameter estimation problems of the multivariable systems described by input–output difference equations. We decompose a multivariable system to several subsystems according to the number of the outputs. Based on the maximum likelihood principle, a maximum likelihood-based recursive least squares algorithm is derived to estimate the parameters of each subsystem. Finally, two numerical examples are provided to verify the effectiveness of the proposed algorithm.     

一种变遗忘因子RLS算法的分析与仿真

自适应信息处理的算法中的RLS算法在信号处理等方面已经得到了大量的应用。首先简要介绍了RLS算法,然后通过对RLS算法中的遗传因子的研究与分析,提出了一种新的变遗忘因子算法,通过修正函数对遗传因子进行修正,实现了此算法的优势,最后对该算法做了仿真试验。试验证明,此算法收敛速度和跟踪效果远好于普通RLS算法,并且系统稳定,具有较强的应用价值。     

Decomposition-Based Recursive Least Squares Algorithm for Wiener Nonlinear Feedback FIR-MA Systems Using the Filtering Theory

A decomposition-based recursive least squares algorithm is developed for Wiener nonlinear systems described by finite impulse response moving average models. After transferring a finite impulse response moving average (FIR-MA) model to a controlled autoregressive model, we compute the parameters by combining the decomposition principle and the least squares method and using the filtering idea. The simulation results validate the proposed algorithm.