Bias compensation methods for stochastic systems with colored noise

For ARX-like systems, this paper derives a bias compensation based recursive least squares identification algorithm by means of the prefilter idea and bias compensation principle. The proposed algorithm can give the unbiased estimates of the system model parameters in the presence of colored noises, and can be on-line implemented. Finally, the advantages of the proposed bias compensation recursive least squares algorithm are shown by simulation tests.     

Recursive computational formulas of the least squares criterion functions for scalar system identification

The paper discusses recursive computation problems of the criterion functions of several least squares type parameter estimation methods for linear regression models, including the well-known recursive least squares (RLS) algorithm, the weighted RLS algorithm, the forgetting factor RLS algorithm and the finite-data-window RLS algorithm without or with a forgetting factor. The recursive computation formulas of the criterion functions are derived by using the recursive parameter estimation equations. The proposed recursive computation formulas can be extended to the estimation algorithms of the pseudo-linear regression models for equation error systems and output error systems. Finally, the simulation example is provided.     

Maximum likelihood least squares identification for systems with autoregressive moving average noise

Maximum likelihood methods are important for system modeling and parameter estimation. This paper derives a recursive maximum likelihood least squares identification algorithm for systems with autoregressive moving average noises, based on the maximum likelihood principle. In this derivation, we prove that the maximum of the likelihood function is equivalent to minimizing the least squares cost function. The proposed algorithm is different from the corresponding generalized extended least squares algorithm. The simulation test shows that the proposed algorithm has a higher estimation accuracy than the recursive generalized extended least squares algorithm.     

功率放大器非线性特性及预失真建模研究

针对无记忆功率放大器的非线性特性及预失真建模的问题,首先建立了多项式模型、极坐标Saleh模型和基于正交三角函数的模型并利用MATLAB对其进行了求解,然后给出了无记忆多项式预失真处理器特性函数表达式及最小二乘解.针对记忆功率放大器的非线性特性及预失真建模的问题,首先建立了记忆多项式模型并对其进行了求解,然后建立了相应的有记忆多项式预失真模型并利用最小二乘法进行了求解,并提出了联合功率放大器特性和输入信号幅值范围的有记忆功放自适应预失真模型.最后求出所给输入信号、输出信号以及加入预失真后线性系统的输出信号的功率谱密度,并计算和比较了信道的带外失真参数ACPR;结果显示,加入预失真后大大提升了系统的性能,线性特性明显加强.     

Auxiliary model based parameter estimation for dual-rate output error systems with colored noise

The dual-rate sampled-data systems can offer better quality of control than the systems with single sampling rate in practice. However, the conventional identification methods run in the single-rate scheme. This paper focuses on the parameter estimation problems of the dual-rate output error systems with colored noises. Based on the dual-rate sampled and noise-contaminated data, two direct estimation algorithms are addressed: the auxiliary model based recursive extended least squares algorithm and the recursive prediction error method. The auxiliary model is employed to estimate the noise-free system output. An example is given to test and illustrate the proposed algorithms.     

Data-driven recursive least squares methods for non-affined nonlinear discrete-time systems

Aiming at identifying nonlinear systems, one of the most challenging problems in system identification, a class of data-driven recursive least squares algorithms are presented in this work. First, a full form dynamic linearization based linear data model for nonlinear systems is derived. Consequently, a full form dynamic linearization-based data-driven recursive least squares identification method for estimating the unknown parameter of the obtained linear data model is proposed along with convergence analysis and prediction of the outputs subject to stochastic noises. Furthermore, a partial form dynamic linearization-based data-driven recursive least squares identification algorithm is also developed as a special case of the full form dynamic linearization based algorithm. The proposed two identification algorithms for the nonlinear nonaffine discrete-time systems are flexible in applications without relying on any explicit mechanism model information of the systems. Additionally, the number of the parameters in the obtained linear data model can be tuned flexibly to reduce computation complexity. The validity of the two identification algorithms is verified by rigorous theoretical analysis and simulation studies.     

On consistency of recursive least squares identification algorithms for controlled auto-regression models

The recursive least squares (RLS) algorithms is a popular parameter estimation one. Its consistency has received much attention in the identification literature. This paper analyzes convergence of the RLS algorithms for controlled auto-regression models (CAR models), and gives the convergence theorems of the parameter estimation by the RLS algorithms, and derives the conditions that the parameter estimates consistently converge to the true parameters under noise time-varying variance and unbounded condition number. This relaxes the assumptions that the noise variance is constant and that high-order moments are existent. Finally, the proposed algorithms are tested with two example systems, including an experimental water-level system.     

Auxiliary model identification method for multirate multi-input systems based on least squares

This paper derives state-space models for multirate multi-input sampled-data systems. Based on the corresponding transfer function models, an auxiliary model based recursive least squares algorithm is presented to identify the parameters of the multirate systems from the multirate input–output data. Further, convergence properties of the proposed algorithm are analyzed. Finally, an illustrative example is given.     

Adaptive Lanczos methods for recursive condition estimation

Estimates for the condition number of a matrix are useful in many areas of scientific computing, including: recursive least squares computations, optimization, eigenanalysis, and general nonlinear problems solved by linearization techniques where matrix modification techniques are used. The purpose of this paper is to propose anadaptiveLanczosestimator scheme, which we callale, for tracking the condition number of the modified matrix over time. Applications to recursive least squares (RLS) computations using the covariance method with sliding data windows are considered.ale is fast for relatively smalln-parameter problems arising in RLS methods in control and signal processing, and is adaptive over time, i.e., estimates at timet are used to produce estimates at timet+1. Comparisons are made with other adaptive and non-adaptive condition estimators for recursive least squares problems. Numerical experiments are reported indicating thatale yields a very accurate recursive condition estimator.Research supported by the US Air Force under grant no. AFOSR-88-0285.Research supported by the US Army under grant no. DAAL03-90-G-105.Research supported by the US Air Force under grant no. AFOSR-88-0285.     

A unified derivation for fast estimation algorithms by the conjugate direction method

Fast estimation algorithms inspired by the classical method of Levinson have been developed in the areas of time series analysis, system identification, and signal processing. This paper provides a unified derivation for the Levinson-Durbin-Whittle-Wiggins-Robinson, fast recursive least squares (RLS), ladder (or lattice), and fast Cholesky algorithms as special cases of the conjugate direction method (CDM). This gives a novel derivation and interpretation for all these methods.     

Recursive prediction error identification of fractional order models

This paper deals with time domain identification of fractional order systems. A new identification technique is developed providing recursive parameters estimation of fractional order models. The identification model is defined by a generalized ARX structure obtained by discretization of a continuous fractional order differential equation. The parameters are then estimated using the recursive least squares and the recursive instrumental variable algorithms extended to fractional order cases. Finally, the quality of the proposed technique is illustrated and compared through the identification of simulated fractional order systems.     

基于主成份分析的递推子空间辨识

研究了多输入多输出系统的状态空间模型的递推子空间辨识问题.针对只有输出量测噪声的线性时不变系统,提出了基于随机逼近-主成份分析(SA-PCA)的估计扩张能观矩阵的递推算法.同时利用递推最小二乘在线估计系统矩阵.最后通过仿真例子说明算法的收敛速度和估计效果.     

On-line identification of parameters of a linear multivariable discrete-time system and their convergence characteristics

A recursive algorithm for on-line identification of the parameters of linear, discrete-time, multi-input, multi-output nondynamical, and dynamical systems using noisy input and output measurements is presented in detail. Necessary and sufficient conditions for the convergence of the recursive algorithm, under certain restrictive assumptions, for arbitrary choice of initial values of the matrices described in the sequel are explicitly derived, which is one of the new results of this paper.     

Neural network for the exact RLS adaptive algorithm

This paper proposes a neural network approach to the implementation of the exact recursive least-squares (RLS) algorithm. We show that the proposed neural network is guaranteed to be stable and to provide the results arbitrarily close to the accurate optimal solution of the RLS algorithm within an elapsed time of only a few characteristic time constants of the network. The parameters of the network (such as interconnections strengths and bias currents) can be obtained from the available data with a few computations, which are much fewer than the computations required in the exact RLS algorithm; as a result, this proposed scheme is very suitable for real time applications of the exact RLS algorithm.     

Three-stage recursive least squares parameter estimation for controlled autoregressive autoregressive systems

A three-stage recursive least squares parameter estimation algorithm is derived for controlled autoregressive autoregressive (CARAR) systems. The basic idea is to decompose a CARAR system into three subsystems, one of which contains one parameter vector, and to identify the parameters of each subsystem one by one. Compared with the recursive generalized least squares algorithm, the dimensions of the involved covariance matrices in each subsystem become small and thus the proposed algorithm has a high computational efficiency. Finally, we verify the proposed algorithm with a simulation example.     

Time-Varying Parameters Prediction

This paper develops a method of adaptive modeling that may be applied to forecast non-stationary time series. The starting point are time-varying coefficients models introduced in statistics, econometrics and engineering. The basic step of modeling is represented by the implementation of adaptive recursive estimators for tracking parameters. This is achieved by unifying basic algorithms—such as recursive least squares (RLS) and extended Kalman filter (EKF)—into a general scheme and next by selecting its coefficients with the minimization of the sum of squared prediction errors. This defines a non-linear estimation problem that may be analyzed in the context of the conditional least squares (CLS) theory. A numerical application on the IBM stock price series of Box-Jenkins illustrates the method and shows its good forecasting ability.     

Optimal estimation of sensor biases for asynchronous multi-sensor data fusion

An important step in a multi-sensor surveillance system is to estimate sensor biases from their noisy asynchronous measurements. This estimation problem is computationally challenging due to the highly nonlinear transformation between the global and local coordinate systems as well as the measurement asynchrony from different sensors. In this paper, we propose a novel nonlinear least squares formulation for the problem by assuming the existence of a reference target moving with an (unknown) constant velocity. We also propose an efficient block coordinate decent (BCD) optimization algorithm, with a judicious initialization, to solve the problem. The proposed BCD algorithm alternately updates the range and azimuth bias estimates by solving linear least squares problems and semidefinite programs. In the absence of measurement noise, the proposed algorithm is guaranteed to find the global solution of the problem and the true biases. Simulation results show that the proposed algorithm significantly outperforms the existing approaches in terms of the root mean square error.     

Adaptive control of linear stochastic evolution systems

An adaptive control problem for some linear stochastic evolution systems in Hilbert spaces is formulated and solved in this paper. The solution includes showing the strong consistency of a family of least squares estimates of the unknown parameters and the convergence of the average quadratic costs with a control based on these estimates to the optimal average cost. The unknown parameters in the model appear affinely in the infinitesimal generator of the C ₀ semigroup that defines the evolution system. A recursive equation is given for a family of least squares estimates and the bounded linear operator solution of the stationary Riccati equation is shown to be a continuous function of the unknown parameters in the uniform operator topology     

On growth factors of the modified Gram–Schmidt algorithm

Growth factors play a central role in studying the stability properties and roundoff estimates of matrix factorizations; therefore, they have attracted many numerical analysts to study upper bounds of these growth factors. In this article, we derive several upper bounds of row‐wise growth factors of the modified Gram–Schmidt (MGS) algorithm to solve the least squares (LS) problem and the weighted LS problem. We also extend the analysis to the MGS‐like algorithm to solve the constrained LS problem. Copyright © 2008 John Wiley & Sons, Ltd.     

Identification of MIMO systems using radial basis function networks with hybrid learning algorithm

When a radial basis function network (RBFN) is used for identification of a nonlinear multi-input multi-output (MIMO) system, the number of hidden layer nodes, the initial parameters of the kernel, and the initial weights of the network must be determined first. For this purpose, a systematic way that integrates the support vector regression (SVR) and the least squares regression (LSR) is proposed to construct the initial structure of the RBFN. The first step of the proposed method is to determine the number of hidden layer nodes and the initial parameters of the kernel by the SVR method. Then the weights of the RBFN are determined by solving a simple minimization problem based on the concept of LSR. After initialization, an annealing robust learning algorithm (ARLA) is then applied to train the RBFN. With the proposed initialization approach, one can find that the designed RBFN has few hidden layer nodes while maintaining good performance. To show the feasibility and superiority of the annealing robust radial basis function networks (ARRBFNs) for identification of MIMO systems, several illustrative examples are included.