基于动态遗忘因子最小二乘与EKF的电池SOC估计

电池荷电状态SOC（State Of Charge）作为电池管理系统中尤为重要的一部分,其准确估计成为锂离子电池研究的重点。为了提高动态工况下的SOC估计精度,对锂离子电池等效模型进行分析,基于AIC（赤池信息）准则确定二阶RC电路为等效电路模型,使用递推最小二乘算法对模型参数进行在线辨识,为提高辨识精度,提出了改进带动态遗忘因子递推最小二乘算法,对算法加入遗忘因子,通过电压结果误差实时动态调整算法遗忘因子取值。将递推最小二乘算法和含动态遗忘因子最小二乘算法分别与扩展卡尔曼滤波（EKF）算法进行SOC联合估计,并对比其预测效果,结果表明含有动态遗忘因子最小二乘与EKF联合估计模型具有更高的精度和鲁棒性。     

Structural identification and software package for linear multivariable systems

This paper is concerned with the structural identification of linear multivariable systems and an interactive identification package. The structural identification is done by taking the time-invariant subsystem from the realizations of the input-output relations identified using data of disjoint time intervals, and the statistical hypothesis test is employed to determine the order, where the input-output relation is identified based on the generalized least squares method using the possibly larger model for the plant. The identification package is for the identification of the input-output relation of a linear multivariable system, for the structural identification based on the realization and for data management.     

Robust nonlinear model identification methods using forward regression

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.     

联合估计稳态算法及其在过程辨识中的应用

时滞和滤波联合估计问题是自适应系统建模和时滞估计两方面的交叉.本文针对 先时滞后滤波的串联模型.提出基于快速横向滤波器的递推最小二乘算法,并以合成氨生产 过程现场数据为例进行模型预测.仿真结果表明,这种结合时滞跟踪的自适应滤波器适于变 时滞低阶系统建模.     

电传飞机飞行品质低阶等效系统算法研究

研究了从飞行试验数据辨识电传飞机动力学模型的低阶等效系统参数的方法。采用频域相干性分析和高精度有限傅立叶变换对试验数据进行了精确的频谱分析。频域方程误差法、输出误差法和单纯形法相结合,为计算电传飞机的低阶等效系统提供了一个实用有效的算法,并使用某型电传飞机试验数据进行了可靠性和有效性验证。结果表明：使用本文的方法辨识的低阶等效系统模型能够准确地描述真实电传飞机的动力学特性。本文的研究内容为电传飞机飞行品质试验方法提供了技术支持。     

递归最小二乘循环神经网络

针对循环神经网络(Recurrent neural networks, RNNs)一阶优化算法学习效率不高和二阶优化算法时空开销过大,提出一种新的迷你批递归最小二乘优化算法.所提算法采用非激活线性输出误差替代传统的激活输出误差反向传播,并结合加权线性最小二乘目标函数关于隐藏层线性输出的等效梯度,逐层导出RNNs参数的迷你批递归最小二乘解.相较随机梯度下降算法,所提算法只在RNNs的隐藏层和输出层分别增加了一个协方差矩阵,其时间复杂度和空间复杂度仅为随机梯度下降算法的3倍左右.此外,本文还就所提算法的遗忘因子自适应问题和过拟合问题分别给出一种解决办法.仿真结果表明,无论是对序列数据的分类问题还是预测问题,所提算法的收敛速度要优于现有主流一阶优化算法,而且在超参数的设置上具有较好的鲁棒性.     

Identification of multi-class linear and nonlinear systems

An algorithm for the identification of multi-class systems which can be described by a class of models over different operating regions is presented. The algorithm involves partitioning the raw data set using discriminant functions followed by parameter estimation. An orthogonal least squares algorithm coupled with a backward elimination procedure is employed for the parameter estimation and data partitioning processes. Provided the data elements are linearly separable, the proposed algorithm will correctly partition the data into the respective classes; parameter estimation algorithms can then be applied to estimate the models associated with each different class. Simulation studies are included to illustrate the algorithm     

A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.     

A study of frequency prediction for power systems

A frequency predictor is identified from simulated measurements of power and frequency on a power system. An on-line Ieast-squares algorithm is used along with a new system structure test for model order identification. A comparison of this system structure test with other model order identification tests is also included. The performance of the resultant predictor is then determined as a function of both the prediction interval and the sampling rate and measurement noise levels on the power and frequency measurements used for the predictor. The results indicate an increase in prediction error with the length of the prediction interval because the predictor loses its principal dependence of "P-f" (power-frequency) dynamics in the power system and depends more strongly on the random load fluctuations over the prediction interval. The modeling error was shown to be unaffected by sampling rate and by measurement noise levels below that of the present power-frequency recorder [2], but was affected by measurement noise levels above the values on the present recorder. This accuracy of the model for small prediction intervals justifies the future use of frequency measurements in power system identification and justifies the use of least-squares algorithms using these measurements. The results on sampling rate and measurement noise imply that the present recorder [2] is an "optimal" design and that the RTDAS [5] will be an even better tool for use in power system model identification.     

Subspace identification of Hammerstein systems using least squares support vector machines

This paper presents a method for the identification of multiple-input-multiple-output (MIMO) Hammerstein systems for the goal of prediction. The method extends the numerical algorithms for subspace state space system identification (N4SID), mainly by rewriting the oblique projection in the N4SID algorithm as a set of componentwise least squares support vector machines (LS-SVMs) regression problems. The linear model and static nonlinearities follow from a low-rank approximation of a matrix obtained from this regression problem.     

Time series prediction using support vector machines,the orthogonal and the regularized orthogonal least-squares algorithms

Generalization properties of support vector machines, orthogonal least squares and zero-order regularized orthogonal least squares algorithms are studied using simulation. For high signal-to-noise ratios (40 dB), mixed results are obtained, but for a low signal-to-noise ratio, the prediction performance of support vector machines is better than the orthogonal least squares algorithm in the examples considered. However, the latter can usually give a parsimonious model with very fast training and testing time. Two new algorithms are therefore proposed that combine the orthogonal least squares algorithm with support vector machines to give a parsimonious model with good prediction accuracy in the low signal-to-noise ratio case.     

Parameter identification of wiener models by multi-innovation algorithms

An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples.     

非整数阶系统频域辨识的递推最小二乘算法

非整数阶系统辨识方法是建立非整数阶系统模型的一种重要工具．本文提出了一种非整数阶系统频域辨识的最小二乘递推算法．给出了算法的详细推导，并用已知系统验证了算法的有效性．结果表明该算法是整数阶系统辨识的最小二乘递推算法的推广．使用此算法，不但能辨识整数阶系统，还能辨识非整数阶系统．     

Comments on "Realization and reduction of Markovian models from nonstationary data"

In the above paper, Baram determines a basis in which to construct realizations from measurement data and for model reduction of these realizations. The basis selected by Baram is similar to the "cost-decoupled" basis introduced earlier [1], [2], with one exception. Both have diagonal state covariances, but the second matrix diagonalized is different. Of course, this makes the two algorithms different and the nature of these differences is pointed out in this correspondence. The principal difference is that Baram's algorithm for model reduction approximates (in a least squares sense) all of the covariance sequences, whereas the model reduction of [1], [2] matches the first two covariance sequences exactly. The cost decoupled basis guarantees several different properties: 1) output covariance matching, and 2) an equivalent quadratic performance metric (i.e., "cost-equivalent" realizations).     

Use of partial least squares within the control relevant identification for buildings

Climate changes, diminishing world supplies of non-renewable fuels, as well as economic aspects are probably the most significant driving factors of the current effort to save energy. As buildings account for about 40 % of global final energy use, efficient building climate control can significantly contribute to the saving effort. Predictive building automation can be used to operate buildings in an energy and cost effective manner with minimum retrofitting requirements. In such a predictive control approach, dynamic building models are of crucial importance for a good control performance. An algorithm which has not been used in building modeling yet, namely a combination of minimization of multi-step ahead prediction errors and partial least squares will be investigated. Subsequently, two case studies are presented: the first is an artificial model of a building constructed in Trnsys environment, while the second is a real-life case study. The proposed identification algorithm is then validated and tested.     

基于LabVIEW的加速度计模型参数辨识模块设计

加速度计的模型参数辨识对研究加速度计动态特性具有重要作用.针对加速度计动态特性测试系统中加速度计模型参数辨识模块的设计,利用LabVIEW图形化编程的特点,以最小二乘原理为基础,通过对加速度计频域响应函数的拟合实现了加速度计模型参数的辨识,并构建了可视化软面板.实验测试表明:所设计模块能够实现加速度计模型参数辨识,有较高的参数辨识精度,并具有良好的人机交互,所辨识出的模型能够描述加速度计的动态特性.     

一类非齐次时变线性模型及自适应预测控制策略

通过分析系统闭环预测存在的缺陷，为了改善系统的预测性能、便于系统辨识和实现自适应控制，提出了一类适合于一般非线性系统的非齐次时变线性模型，给出并证明了其存在性定理。而后给出了这类非齐次时变线性模型的渐消记忆递推最小二乘参数估计，推出了基于该类模型的自适应预测控制算法。仿真结果表明基于该类模型的自适应预测控制策略具有优良的控制品质。     

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.     

Elitist teaching–learning-based optimization (ETLBO) with higher-order Jordan Pi-sigma neural network: a comparative performance analysis

An adaptive p-step prediction model for nonlinear dynamic processes is developed in this paper and implemented with a radial basis function (RBF) network. The model can predict output for multi-step-ahead with no need for the unknown future process output. Therefore, the long-range prediction accuracy is significantly enhanced and consequently is especially useful as the internal model in a model predictive control framework. An improved network structure adaptation is also developed with the recursive orthogonal least squares algorithm. The developed model is online updated to adapt both its structure and parameters, so that a compact model structure and consequently a less computing cost are achieved with the developed adaptation algorithm applied. Two nonlinear dynamic systems are employed to evaluate the long-range prediction performance and minimum model structure and compared with an existing PSC model and a non-adaptive RBF model. The simulation results confirm the effectiveness of the developed model and superior over the existing models.

     

Least squares based iterative identification for a class of multirate systems

This paper studies modeling and identification problems for multi-input multirate systems with colored noises. The state-space models are derived for the systems with different input updating periods and furthermore the corresponding transfer functions are obtained. To solve the difficulty of identification models with unmeasurable noises terms, the least squares based iterative algorithm is presented by replacing the unmeasurable variables with their iterative estimates. Finally, the simulation results indicate that the proposed iterative algorithm has advantages over the recursive algorithms.