Continuous-time Wiener system identification

A continuous-time Wiener system is identified. The system consists of a linear dynamic subsystem and a memoryless nonlinear one connected in a cascade. The input signal is a stationary white Gaussian random process. The system is disturbed by stationary white random Gaussian noise. Both subsystems are identified from input-output observations taken at the input and output of the whole system. The a priori information is very small and, therefore, resulting identification problems are nonparametric. The impulse impulse of the linear part is recovered by a correlation method, while the nonlinear characteristic is estimated with the help of the nonparametric kernel regression method. The authors prove convergence of the proposed identification algorithms and examine their convergence rates     

Combined parametric-nonparametric identification of Hammerstein systems

A novel, parametric-nonparametric, methodology for Hammerstein system identification is proposed. Assuming random input and correlated output noise, the parameters of a nonlinear static characteristic and finite impulse-response system dynamics are estimated separately, each in two stages. First, the inner signal is recovered by a nonparametric regression function estimation method (Stage 1) and then system parameters are solved independently by the least squares (Stage 2). Convergence properties of the scheme are established and rates of convergence are given.     

Frequency-domain identification of linear systems using arbitraryexcitations and a nonparametric noise model

Presents a generalized frequency domain identification method to identify single-input/single-output (SISO) systems combining two previously published extensions in one method: arbitrary but persistent excitations are allowed and a nonparametric noise model is extracted from the same data that are used to identify the system. The method is directly applicable to identification in feedback if an external persistently exciting reference signal is available     

Augmented hybrid method for continuous process identification from sampled data with coloured noise

The identification of a linear continuous-time model for a multivariable dynamic system from sampled input-output observations is considered. An augmented hybrid parametric method is proposed to overcome the interference of the coloured output noise in the sampled output. The parameters of the continuous-time process model are estimated from an augmented input-output realization which utilizes the dynamic information of the discrete-time noise model. Numerical examples are presented to illustrate how to obtain an adequate dynamic process model, considering the coloured output noise, by using a discrete-time noise model.     

The Laplace Image of Random Processes and Nonparametric Identification of Continuous Systems

The Laplace image of stationary random normal processes is studied. The covariance function of the Laplace image of white noise is converted by a linear shaping filter into the covariance function of the Laplace image of the filter output process. The relationship between the covariance function of the Laplace image of a random process and the autocovariance function and spectral density is determined. The covariance function of the Laplace image of measurement errors of a transition process in a stationary linear system is applied in optimal nonparametric identification of the transfer function.     

Comparison of the closed-loop identification methods in terms of the bias distribution

Several schemes for plant model identification in closed-loop operation including classical direct method, two-step identification and closed-loop output error algorithms are considered. These methods are analyzed and compared in terms of the bias distribution of the estimates for the case that the noise model is estimated as well as the case that a fixed model of noise is considered (output error structure). The problems concerning the filtered direct method which is often used in the iterative identification and control scheme are mentioned. It is shown that these problems may be solved by the closed-loop output error identification method.     

基于组合式信号源的Hammerstein-Wiener模型辨识方法

针对含有有色噪声的非线性Hammerstein-Wiener模型,提出一种基于组合式信号源的辨识方法.通过利用可分离信号和随机信号组成的组合信号源实现有色噪声干扰下Hammerstein-Wiener模型各串联模块参数辨识的分离,简化辨识过程.首先,基于可分离信号的输入和输出,采用相关分析方法抑制过程噪声的干扰,辨识输出静态非线性模块和动态线性模块的参数;然后,基于辅助模型技术,利用辅助模型的输出和残差的估计值分别取代辨识模型中的不可测中间变量和噪声变量,推导辅助模型递推增广最小二乘方法,根据随机信号的输入输出数据辨识输入静态非线性模块和噪声模型的参数;最后,通过理论分析和仿真结果表明,所提出方法能够有效辨识有色噪声干扰下的非线性Hammerstein-Wiener模型,具有较好的鲁棒性.     

III-posed and well-posed problems in systems identification

Impulse response identification almost always leads to an ill-posed mathematical problem. This fact is the basis for the well-known numerical difficulties of identification by means of the impulse response. The theory of regularizable ill-posed problems furnishes a unifying point of view for several specific methods of impulse response identification. In this paper we introduce a class of input/output representations, which we call λ-representations, for linear, time-invariant systems. For many cases of practical interest the identification of one of these representations is mathematically well-posed. Its determination is thus relatively insensitive to certain experimental uncertainties, and rational error-in-identification bounds may be found, so that λ-identification is often an attractive alternative to impulse response identification in the nonparametric modeling of physical systems which must be identified from input/ output records. We investigate the effects of input and output uncertainties (noise) on λ-identification, and discuss the problem of finding minimal realizations from these representations. We illustrate the work with an example of electromagnetic pulse (EMP) threat prediction using experimental data. Hard error bounds are provided on the predicted threat. For this problem, the appropriate λ-representation turns out to be the ramp response.     

非参数模型控制在液位控制系统中的应用研究

针对工业控制过程中液位系统的时变和明显的滞后特征,研究了非参数模型控制方法在液位控制系统中的设计方案,讨论了控制算法中引入的伪偏导数的在线估计问题,实现了通过液位系统的输入输出信息并利用递归最小二乘法对伪偏导数进行在线估计的过程,仿真实验验证了非参数模型算法对液位控制的鲁棒性、快速性及抗干扰性,通过仿真比较,展示了该算法性能优于PID算法和模糊控制的结果.     

有色噪声干扰下Hammerstein非线性系统两阶段辨识

针对实际工业过程中普遍存在有色噪声,提出了有色噪声干扰下Hammerstein非线性系统两阶段辨识方法。采用设计的组合式信号实现Hammerstein系统各模块参数辨识分离,简化了辨识过程。在第一阶段,基于可分离信号的输入输出数据,利用相关分析算法估计线性模块参数,减少了有色噪声对辨识的干扰。在第二阶段,基于随机信号的输入输出数据,在最小二乘算法中引入滤波技术,推导了滤波递推增广最小二乘算法,提高了非线性模块参数和噪声模型参数的辨识精度。仿真结果表明：提出的两阶段辨识方法提高了辨识精度,有效地抑制了有色噪声的干扰。     

Frequency domain identification of Hammerstein models

Discusses Hammerstein model identification in the frequency domain using sampled input-output data. By exploring the fundamental frequency and harmonics generated by the unknown nonlinearity, we propose a frequency domain approach and show its convergence for both the linear and nonlinear subsystems in the presence of noise. No a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric.     

面向工业过程的建模辨识平台的设计与实现

提出了一种面向工业过程的可视化建模辨识平台的设计和实现方法.该平台加载了多种辨识算法,并使用OPC技术和各类工控系统进行数据交互,以实现对复杂工业系统的动态特性测试.基于输入输出数据,获得系统的参数模型或非参数模型.对实际工业对象建模辨识的结果,表明该平台大大提高了建模的效率和精度.     

Simulation of noise figure of nonlinear amplifiers using the orthogonalization of the nonlinear model

A simulation procedure of noise figure (NF) of nonlinear amplifiers is developed. NF is defined in terms of the effective signal‐to‐noise ratio (SNR) at the output of a nonlinear amplifier. The effective output SNR when the input consists of a communication signal plus Gaussian noise is evaluated through the identification of the effective output noise and nonlinear distortion power using the orthogonalization of the nonlinear model. The approach is useful for the assessment of noise performance of low‐noise amplifiers in wireless systems. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE 2009.     

On system identification of complex systems from finite data

We introduce a new principle for identification based on choosing a model from the model-parameterization, which best approximates the unknown real system belonging to a more complex space of systems that do not lend themselves to a finite-parameterization. The principle is particularly effective for robust control as it leads to a precise notion of parametric and nonparametric error, and the identification problem can be equivalently perceived as that of robust convergence of the parameters in the face of unmodeled errors. The main difficulty in its application stems from the interplay of noise and unmodeled dynamics and requires developing novel two-step algorithms that amount to annihilation of the unmodeled error followed by averaging out the noise. The paper establishes: robust convergence for a large class of systems, topologies, and unmodeled errors; sample path based finite-time polynomial rate of convergence; and annihilation-correlation algorithms for linearly parameterized model structures     

基于非参数直方图模型的鲁棒说话人识别算法

建立一种非参数模型来刻画说话人的特征分布,并采用地面移动距离来度量分布之间的相似性.该方法能有效地利用有限的数据表达说话人的身份信息,直接计算特征分布与测试语音分布之间的距离,与传统的矢量量化和高斯混合模型相比,不需要通过对所有语音帧计算总平均失真误差和最小相似度,计算简单,主要能够降低系统对数据量的依赖性.并且通过自适应直方图均衡化方法对原始语音特征进行修正,使得噪声环境下获得的语音特征经过修正后更符合真实分布,增强了特征的抗噪性.实验表明,本文提出的方法在噪声环境下的短语音说话人识别系统中表现出较强的优势.     

Asymptotic properties of set membership identification algorithms

This paper addresses the asymptotic worst-case properties of set membership identification (SMID) algorithms. We first present a set membership identification algorithm which can be used with a model structure consisting of parametric and nonparametric uncertainty, as well as output additive disturbances. This algorithm is then studied in the context of asymptotic worst-case behavior. We derive lower bounds on the worst-case achievable identification error measured by the volume, as well as the sum-of-sidelengths of the identified ellipsoidal uncertainty sets. We then show that there exist inputs which can shrink the uncertainty sets to these lower bounds.     

A cumulant based algorithm for the identification of input-output quadratic systems

The identification of a special class of polynomial models is pursued in this paper. In particular a parameter estimation algorithm is developed for the identification of an input-output quadratic model excited by a zero mean white Gaussian input and with the output corrupted by additive measurement noise. Input-output crosscumulants up to the fifth order are employed and the identification problem of the unknown model parameters is reduced to the solution of successive triangular linear systems of equations that are solved at each step of the algorithm. Simulation studies are carried out and the proposed methodology is compared with two least squares type identification algorithms, the output error method and a combination of the instrumental variables and the output error approach. The proposed cumulant based algorithm and the output error method are tested with real data produced by a robotic manipulator.     

广义输出误差模型的两阶段最小二乘递推辨识

在有色噪声干扰系统中有一类系统, 它具有广义输出误差模型(OEARMA), 本文提出一类广义输出误差模型的 两阶段递推最小二乘参数估计算法. 该算法基本思想是结合辅助模型辨识思想和分解技术, 将系统分解成两个子系统, 每个子系统包含一个参数向量. 借助基于辅助模型和递推最小二乘理论, 用辅助模型的输出代替辨识模型信息向量中未 知中间变量, 用估计残差代替信息向量中不可测噪声项, 从而可以运用递推辨识思想来估计系统所有参数. 该算法具有 较高的计算效率, 仿真例子说明提出算法的有效性.     

Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open- and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method.