耦合多新息随机梯度辨识方法性能分析

针对一类耦合参数多变量系统, 提出一种耦合多新息随机梯度方法. 通过该方法进行参数辨识并对该方法进行性能分析. 该方法的基本思路在于利用历史新息中包含的信息, 将耦合随机梯度算法中的新息项扩展为多新息向量, 从而提升耦合随机梯度算法中单个子系统的辨识效果. 仿真结果表明, 通过增加新息长度可以提升辨识结果的收敛速度和精度.

     

多新息随机梯度辨识方法

多新息随机梯度辨识方法是系统辨识和参数估计的一种基本方法.该方法由于采用了间断迭代,因此可以克服坏数据对参数估计的影响,且具有较强的鲁棒性,又可以跟踪时变参数.作者从理论上给出了多新息随机梯度辨识方法的推导过程,同时列出多新息随机梯度辨识方法的各种变形.数字仿真实验表明多新息随机梯度辨识方法具有良好的性能.     

多变量系统辅助模型多新息随机梯度辨识方法

针对多变量输出误差系统的模型辨识问题,借助辅助模型思想推导出其随机梯度辨识算法;由于该算法的收敛速度慢,为了提高收敛速度,将算法中的新息向量扩展成新息矩阵,得到基于辅助模型的多新息随机梯度辨识算法;辅助模型多新息算法使用新息矩阵对参数进行校正估计,该新息矩阵不仅包含了当前时刻的新息向量,还包含过去多个时刻的新息向量,因而,与辅助模型随机梯度算法和增广随机梯度算法相比,该算法具有更快的收敛速度;一个二输入二输出的仿真例子证明了所提出的算法的确具有更快的收敛速度.     

基于系统辨识算法的风力机桨距系统故障诊断

针对风力机桨距系统故障,提出一种基于观测器的多新息随机梯度辨识算法的故障诊断方法．多新息随机梯度辨识算法通过扩展新息长度能够改进随机梯度辨识算法的估计精度,根据系统的规范状态空间模型,结合状态观测器可以实现系统状态和参数的交互估计．将桨距系统模型转换为可辨识的状态空间模型,依据桨距系统故障会引起系统参数变化的特点,采用所提出的算法对系统状态和参数进行估计,将桨距系统故障诊断问题转化为系统状态和参数估计问题．仿真结果表明,所提出的方法能够有效诊断桨距系统故障．     

基于移动数据窗的传递函数多新息随机梯度辨识方法

一些工业过程可以近似用一个传递函数描述,结合统计辨识方法和非线性优化策略提出传递函数参数辨识方法.该方法采用动态数据方案,使用系统观测数据获得系统更多的模态信息.基于动态观测数据,提出传递函数随机梯度参数辨识方法.为进一步提高辨识精度,利用动态窗数据将随机梯度参数辨识方法中的标量新息扩展为新息向量,提出传递函数多新息随机梯度参数估计方法.最后通过仿真例子对所提出的方法进行了性能分析和模型验证.     

变遗忘因子多新息随机梯度算法双馈电机参数辨识

针对电机运行过程中参数变化特点,基于多新息辨识理论与随机梯度辨识算法理论,结合变遗忘因子,提出了基于变遗忘因子多新息随机梯度算法的双馈电机参数辨识方法。该方法考虑到双馈电机非线性强耦合,采用定子磁链定向的矢量控制技术,搭建双馈电机矢量控制系统采集数据,并推导dq坐标系下电机参数辨识模型的标准形式,根据算法辨识出电机电感及电阻参数。仿真结果验证了该算法的有效性。     

MISO系统辅助模型多新息增广随机梯度算法

对于有色噪声干扰的输出误差多输入单输出(MISO)系统,常规的递推最小二乘辨识方法给出的参数估计是有偏的.为了提高随机梯度辨识方法的收敛精度和速度,用辅助模型的输出代替辨识模型信息向量中的未知不可测变量,推导出其辅助模型增广随机梯度辨识算法;再引入新息长度扩展标量新息为新息向量,提出了基于辅助模型的MISO系统多新息增广随机梯度辨识算法.所得算法在每一次的迭代中不仅使用了当前数据和新息,而且使用了过去数据和新息,提高了参数估计精度和收敛速度.仿真例子验证了算法的有效性.     

基于滤波的分段线性Hammerstein系统的递推辨识方法

Hammerstein模型具有结构简单、能很好地反映典型非线性特性等优点, 一直是控制领域的重要研究内容之一. 本文主要研究输出误差自回归Hammerstein系统的辨识问题, 系统的输入非线性部分采用分段线性函数拟合,并引入切换函数和位置函数将其表示为线性参数表达式. 为克服有色噪声的干扰, 本文通过关键项分离和数据滤波技术, 建立系统的滤波辨识模型. 在此基础上, 文中提出了基于滤波的遗忘梯度算法, 基于滤波的递推广义最小二乘算法和基于滤波的多新息遗忘梯度算法估计未知参数. 本文通过仿真实例验证了所提算法的有效性, 证明了多新息理论的应用可以有效地提高递推算法的辨识性能.     

非均匀Hammerstein-Wiener 系统的递阶随机梯度辨识算法

针对一类非均匀数据采样Hammerstein-Wiener 系统, 提出一种递阶多新息随机梯度算法. 首先基于提升技术, 推导出系统的状态空间模型, 并考虑因果约束关系, 将该模型分解成两个子系统, 利用多新息遗忘随机梯度算法辨识出此模型的参数; 然后, 引入可变遗忘因子, 提出一种修正函数并在线确定其大小, 提高了算法的收敛速度及抗干扰能力. 仿真实例验证了所提出算法的有效性和优越性.

     

基于多新息最小二乘算法的非线性系统辨识

针对最小二乘算法辨识性能较差问题,将最小二乘算法中的单新息通过利用p组数据拓展到多新息向量,提出了多新息最小二乘算法。与最小二乘相比,所提出的算法不仅利用了当前的系统信息,而且利用了过去的系统信息,进一步提高了参数辨识的精度和收敛速度。在所提出的算法中,为了减少冗余的参数辨识和算法计算量,利用关键性分离技术构造整体辨识模型。设计了辅助模型来替代系统中未知的中间变量,提高了参数估计的精度。对比仿真结果表明,所提出的算法具有比递归最小二乘算法更高的辨识精度和收敛速度。     

Auxiliary model-based RELS and MI-ELS algorithm for Hammerstein OEMA systems

This paper considers the identification problem for Hammerstein output error moving average (OEMA) systems. An auxiliary model-based recursive extended least-squares (RELS) algorithm and an auxiliary model-based multi-innovation extended least-squares (MI-ELS) algorithm are presented using the multi-innovation identification theory. The basic idea is to express the system output as a linear combination of the parameters by using the key-term separation principle and auxiliary model method. The proposed algorithms can give highly accurate parameter estimates. The simulation results show the effectiveness of the proposed algorithms.     

Recursive Identification Methods for Multivariate Output-error Moving Average Systems Using the Auxiliary Model

This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well.     

Decomposition-based recursive least squares identification methods for multivariate pseudo-linear systems using the multi-innovation

This paper studies the parameter estimation algorithms of multivariate pseudo-linear autoregressive systems. A decomposition-based recursive generalised least squares algorithm is deduced for estimating the system parameters by decomposing the multivariate pseudo-linear autoregressive system into two subsystems. In order to further improve the parameter accuracy, a decomposition based multi-innovation recursive generalised least squares algorithm is developed by means of the multi-innovation theory. The simulation results confirm that these two algorithms are effective.     

Performance analysis of multi-innovation stochastic Newton recursive algorithms

The stochastic Newton recursive algorithm is studied for system identification. The main advantage of this algorithm is that it has extensive form and may embrace more performance with flexible parameters. The primary problem is that the sample covariance matrix may be singular with numbers of model parameters and (or) no general input signal; such a situation hinders the identification process. Thus, the main contribution is adopting multi-innovation to correct the parameter estimation. This simple approach has been proven to solve the problem effectively and improve the identification accuracy. Combined with multi-innovation theory, two improved stochastic Newton recursive algorithms are then proposed for time-invariant and time-varying systems. The expressions of the parameter estimation error bounds have been derived via convergence analysis. The consistence and bounded convergence conclusions of the corresponding algorithms are drawn in detail, and the effect from innovation length and forgetting factor on the convergence property has been explained. The final illustrative examples demonstrate the effectiveness and the convergence properties of the recursive algorithms.     

A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems

The identification of nonlinear systems is a hot topic in the identification fields. In this paper, a data filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein nonlinear controlled autoregressive moving average systems by adopting the key-term separation principle and the data filtering technique. The proposed algorithm provides a reference to improve the identification accuracy of the nonlinear systems with colored noise. The simulation results show that the new algorithm can more effectively estimate the parameters of the Hammerstein nonlinear systems than the multi-innovation stochastic gradient algorithm.     

基于多新息理论的PID神经网络改进算法

为了提高非线性动态系统辨识精度,提出一种基于多新息理论的PID神经网络改进算法。对具有时间延迟非线性动态系统,由于采用多新息,充分利用了系统的当前数据和历史数据,较传统的BP算法,本文所提算法在辨识精度和收敛速度方面具有更好的效果。仿真结果表明该算法的有效性。     

Several multi-innovation identification methods

This paper considers connections between the cost functions of some parameter identification methods for system modelling, including the well known projection algorithm, stochastic gradient (SG) algorithm and recursive least squares (RLS) algorithm, and presents a modified SG algorithm by introducing the convergence index and a multi-innovation projection algorithm, a multi-innovation SG algorithm and a multi-innovation RLS algorithm by introducing the innovation length, aiming at improving the convergence rate of the SG and RLS algorithms. Furthermore, this paper derives an interval-varying multi-innovation SG and an interval-varying multi-innovation RLS algorithm in order to deal with missing data cases.     

Maximum likelihood stochastic gradient estimation for Hammerstein systems with colored noise based on the key term separation technique

This paper considers the identification problems of Hammerstein finite impulse response moving average (FIR-MA) systems using the maximum likelihood principle and stochastic gradient method based on the key term separation technique. In order to improve the convergence rate, a maximum likelihood multi-innovation stochastic gradient algorithm is presented. The simulation results show that the proposed algorithms can effectively estimate the parameters of the Hammerstein FIR-MA systems.