一种新的线性分布参数系统辨识方法

本文应用块脉冲函数（ＢＰＦ）将含有未知参数的线性分布参数系统偏微分方程转换为矩阵方程，通过最小二乘法辨识线性分布参数系统的参数，辨识结果表明，ＢＰＦ辨识法具有原理简捷，实现方便，辨识精度高等优点。     

利用调制函数法的模糊模型参数辨识

利用调制函数法辨识非线性连续系统的模糊模型参数.系统的动力学微分方程存在微分项,通过输入输出数据辨识模糊模型参数时不能忽略扰动的影响,因此辨识模糊模型参数比较困难.利用调制函数法可以消除微分项,通过无微分项的联立方程的求解容易进行模糊模型参数辨识.几个非线性连续系统的仿真实验验证了所设计的利用调制函数法的模糊模型参数辨识的正确性和有效性.     

基于去噪优化的动力系统关键参数辨识算法

由于传统方法没有对模型噪声实现有效处理,导致辨识精度与辨识速度较低,为此提出基于调制函数法的动力学系统参数辨识算法.对模型噪声进行表示与替换,通过调制函数法构造辨识模型,通过递推算法对辨识模型进行处理以实现噪声的处理.为构造出与动力学系统模型相一致的参数辨识模型,以动力学系统模型为基础对参数辨识模型的神经网络拓扑结构进...     

基于正交函数的立式淬火炉控制系统参数辨识

大型立式淬火炉体积庞大,工况复杂,炉内温度分布呈本征非均匀性.为了获得温度控制高精度和高均匀性提出参数辨识算法,包括求解正交函数正、反向积分运算矩阵,以块脉冲函数为基函数利用正交函数变换将由偏微分方程描述的分布参数系统模型转化为最小二乘形式的代数方程.辨识过程中考虑了大型立式淬火炉温度分布参数系统模型边界条件和初始条件的影响,提高了参数辨识精度,算法计算量小且保持了系统的空间分布特性.     

基于连锁聚类法及遗传算法的模糊建模

模糊建模可以分为被辨识系统的结构辨识和参数辨识.针对系统的结构辨识,提出了一种新型连锁聚类算法,用其来实现被辨识系统的结构辨识及初始参数辨识;针对系统的参数辨识,提出了采用遗传算法对被辨识系统的参数进行更加精确的校正.通过结构辨识算法和参数辨识算法的结合,可以只针对被辨识系统的输入输出测试数据直接进行被辨识系统的结构辨识及参数的进一步精确校正.通过对非线形函数的仿真结果表明,此辨识方法具有较好的辨识结果.     

一种Hammerstein-Wiener系统的递归辨识算法

针对含有过程噪声的Hammerstein-Wiener系统,本文提出一种递归辨识算法用于系统的在线辨识. 首先使用多项式函数对系统非线性部分进行严格参数化,在此基础上以参数误差平方和的期望值最小为目标函数,推导出参数估计的递归更新公式,避免了过程噪声对辨识结果的影响. 通过对算法进行深入分析,得到参数一致收敛的条件,并给出算法中重要系数的设定方法,使参数收敛域得到扩大. 与传统两阶段法的数值仿真比较验证了该方法的优越性.     

分布参数系统的时空ARX建模及预测控制

本文针对一类可由抛物型偏微分方程描述的分布参数系统,研究了一种基于输入输出数据的建模与控制方法.首先利用Karhunen-Loève（K-L）分解提取系统的一组主导空间基函数,并以此对系统输出进行时空分解,随后由时空分解得到的时间系数部分以及系统激励构成输入输出信息,利用最小二乘法辨识出时域ARX模型,最后针对该模型设计了广义预测控制器.仿真结果表明,上述控制方法能够对分布参数系统取得良好的控制效果.     

利用递推增广矩阵辨识加速度计的参数

介绍了加速度计的基本工作原理和结构模型,将加速度计的微分方程转换成差分方程,在传统最小二乘法辨识的基础上,采用递推增广矩阵的辨识方法对加速度计的参数模型进行辨识.通过Matlab对其仿真,得到被辨识参数的估计值与曲线图,说明采用递推增广矩阵辨识方法辨识系统参数具有辨识速度快、辨识精度高、辨识结果准确等特点.     

心肌收缩性异长自身调节的参数辨识(英文)

应用系统辨识方法,研究了心肌收缩性异长自身调节的动态过程。这种动态过程可由二阶微分方程描述,并用Gauss-Newton法求出方程的参数a、b、c与d,探讨了各参数的的意义,指出参数c对于判断系统的稳定性有十分重要的意义,从而估计心肌的收缩功能。     

动态系统时变参数的辨识

本文给出了跟踪动态系统时变参数的一种简单而有效的算法,引出了多输出系统输出可 分离性的概念,说明在辨识的过程中,在适当条件下,n输出系统可分解成n个一定意义下与 之等效的单输出系统,这种方法将给时变参数的辨识带来方便.本文的结果主要应用于预报 模型的辨识.     

动态系统"类等效"模型测辩及多可调参数的Padé模型降阶

本文提出了一种时域和频域测辨相结合的线性定常系统测辨新方法.当高阶系统传递函 数已知时,它就是具有多可调参数的Padé逼近.此法由测辨高阶系统前n阶时间矩和测辨 描述系统主要动态性能的一些典型频率响应数据,确定动态系统"类等效"简化模型的参数. 此外,"外推函数"的使用有效地提高了时矩测辨的精度.实例表明,本方法简易、精确和灵活, 适于工程建模,便于工程设计.     

ARMAX lattice algorithm for identification and prediction of dynamic systems

A new ARMAX lattic predictor is developed for identification and prediction of dynamic systems having unknown input and time delay from short records. It is based on the Levinson recursion scheme of the AR and ARMA lattice algorithms, without introducing stability problems or excessively increasing the computation. The cascaded structure of the lattice form, consisting of identical sections, is very convenient for implementation using special purpose hardware and microprocessors. The computational properties of the proposed predictor are discussed and compared with the well known extended recursive least-squares algorithm. The developed algorithm is tested on real, short records, obtained from an economic dynamic system.     

瞬态表面温度传感器动态校准及误差补偿研究

测量瞬态高温时,由于传感器自身的热惯性,测量结果与真实结果之间存在很大的动态误差。动态补偿对于改善测温系统动态特性,减小动态误差有重要意义。该文首先熟悉现有的瞬态表面温度传感器动态校准系统;然后,利用系统所测得输人输出数据,采用系统辨识方法建立了测温系统的动态数学模型,并利用交叉检验法验证该模型的正确性;最后,利用反滤波动态补偿方法实现对瞬态表面温度传感器测温系统的动态补偿。经检验该方法可以达到理想的补偿效果,减小了动态误差,改善了系统的动态特性。     

瞬态表面温度传感器动态建模方法研究

测量瞬态高温时,由于传感器自身的热惯性,测量结果与真实结果之间存在很大的动态误差.动态补偿或动态误差修正对于改善测温系统动态特性,减小动态误差有重要意义,而建立温度传感器动态数学模型则是进行动态补偿或动态误差修正的前提.本文首先设计了瞬态表面温度传感器动态校准系统;然后,利用系统所测得输入输出数据,采用系统辨识方法建立了测温系统的动态数学模型;最后,利用交叉检验法验证该模型的正确性.经检验该方法可以达到理想的辨识效果,从而为系统反滤波动态误差修正奠定了基础.     

Dynamic neural networks with hybrid structures for nonlinear system identification

Dynamic neural networks (DNNs) have important properties that make them convenient to be used together with nonlinear control approaches based on state space models and differential geometry, such as feedback linearisation. However the mapping capability of DNNs are quite limited due to their fixed structure, that is, the number of layers and the number of hidden units. An example shown in this paper has demonstrated this limitation of DNNs. The development of novel DNN structures, which has good mapping capability, is a relevant challenge being addressed in this paper. Although the structure is changed minorly only, the mapping capability of the new designed DNN in this paper has been improved dramatically. Previous work [J. Deng et al., 2005. The dynamic neural network of a hybrid structure for nonlinear system identification. In: 16th IFAC World Congress, Prague.] presents a new dynamic neural network structure which is suitable for the identification of highly nonlinear systems, which needs the outputs from the real system for training and operation. This paper presents a hybrid dynamic neural network structure which presents a similar idea of serial–parallel hybrid structure, but it uses an output from another neural network for training and operation classified as a serial–parallel model. This type of DNNs does not require the output of the plant to be used as an input to the model. This neural network has the advantages of good mapping capabilities and flexibilities in training complicated systems, compared to the existed DNNs. A theoretical proof showing how this hybrid dynamic neural network can approximate finite trajectories of general nonlinear dynamic systems is given. To illustrate the capabilities of the new structure, neural networks are trained to identify a real nonlinear 3D crane system.     

Modular flight control reconfiguration design and simulation

This paper describes a reconfiguring flight control algorithm for damaged aircraft based upon a modular approach. This approach combines real time physical model identification with adaptive nonlinear dynamic inversion (NDI). The sensitivity of NDI to modeling errors is eliminated here by making use of a real time identified model of the aircraft. In failure situations, the damaged aircraft model is identified by the two step method and this updated model is supplied to the model-based adaptive NDI routine, which reconfigures for the fault in real time. Reconfiguration test results for damaged aircraft models indicate good fault handling capabilities of this fault tolerant control set-up, for component as well as structural faults.     

用多项式预测滤波消噪的传感器动态特性辨识

在利用传感器进行动态测量时,为了得到精确的测量结果,需要建立传感器动态特性的数学模型,传感器动态特性可以通过系统辨识得到.但是,测量噪声的存在,使得辨识得到的传感器动态特性与实际动态特性存在一定误差,影响到测量系统的精度.为了解决该问题,本文讨论了多项式预测滤波和中值滤波相结合的方法对传感器输出信号进行滤波消噪.然后,利用消噪后的信号,通过系统辨识方法建立传感器动态特性的数学模型.研究表明,采用本文研究的方法可以克服测量噪声对传感器动态特性辨识的影响,并将该方法用于薄膜热电偶的动态特性辨识.     

Constrained state-space system identification with application to structural dynamics

Constrained identification of state-space models representing structural dynamic systems is addressed. Based on physical insight, transfer function constraints are formulated in terms of the state-space parametrization. A simple example shows that a method tailored for this application, which utilizes the non-uniqueness of a state-space model, outperforms the classic sequential quadratic programming method in terms of robustness and convergence properties. The method is also successfully applied to real experimental data of a plane frame structure.     

Identification of nonlinear dynamic models of electrostatically actuated MEMS

This paper focuses on the identification of nonlinear dynamic models for physical systems such as electrostatically actuated micro-electro-mechanical systems (MEMS). The proposed approach consists in transforming, by means of suitable global operations, the input–output differential model in such a way that the new equivalent formulation is well adapted to the identification problem, thanks to the following properties: first, the linearity with respect to the parameters to be identified is preserved, second, the continuous dependence on noise measurements is restored. Consequently, a simple least-square resolution can be used, in such a way that some of the difficulties classically encountered with identification methods are by-passed. The method is implemented on real measurement data from a physical system.