一种辨识Wiener-Hammerstein模型的新方法

针对非线性Wiener-Hammerstein模型,提出利用粒子群优化算法对非线性模型进行辨识的新方法.该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题;然后采用粒子群优化算法获得该优化问题的解.为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法.最后,仿真结果验证了该方法的有效性和可行性.     

基于改进粒子群算法的Hammerstein模型辨识

提出辨识非线性Hammerstein模型的新方法。将非线性系统的辨识问题转化为参数空间上的函数优化问题,采用粒子群算法获得该优化问题的解。为了进一步增强粒子群优化算法的辨识性能,提出采用速度变异粒子群对整个参数空间进行搜索得到系统参数的最优估计。仿真结果验证了该方法的有效性。     

基于自适应云粒子群算法的Wiener模型辨识

针对非线性系统Wiener模型的系统辨识问题,提出一种基于自适应云模型的粒子群优化(ACMPSO)算法的辨识方法。ACMPSO算法利用云模型实现优秀粒子的遗传和进化操作,根据进化状况动态调整云模型的参数,自适应地控制云模型算法的寻优范围和精度,有较强的全局搜索和局部求精能力。仿真实验证明该算法寻优精度高于其他主要PSO算法;将该算法应用于Wiener模型的系统辨识,通过实验证明了该辨识方法优于当前其他方法。     

采用改进PSO的非线性系统T-S模糊模型辩识

提出了一种新的T-S模糊模型的非线性系统辨识方法。采用自适应模糊C均值聚类算法确定模糊模型的前件结构及参数,用改进的粒子群优化(PSO)算法来辩识模糊模型的结论参数以获得系统参数的最优估计。仿真结果表明该方法是有效的。     

应用改进粒子群算法辨识Hammerstein模型

研究非线性系统辨识问题.针对非线性系统中单输入单输出Hammerstein模型,由于传统辨识方法对Hammerstein模型中非线性部分具有不易辨识的缺陷,造成辨识精度低、辨识效果差等问题.为此,在基本粒子群算法的基础上,提出了一种带有收缩因子的改进的粒子群算法对非线性系统进行辨识的方法,可将参数辨识问题转换为参数空间上的函数优化问题,然后利用粒子群算法的并行搜索能力进行参数寻优.通过MATLAB软件进行仿真,并与基本粒子群算法进行比较,结果表明,利用改进算法不仅提高了辨识精度而且获得了良好的辨识效果,从而验证了算法的有效性和可行性.     

一种辨识Wiener-Hammerstein模型的新方法

针对非线性Wiener-Hammerstein模型,提出利用粒子群优化算法对非线性模型进行辨识的新方法.该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题;然后采用粒子群优化算法获得该优化问题的解.为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法.最后,仿真结果验证了该方法的有效性和可行性.

     

基于改进粒子群优化算法的MFAC参数寻优

针对无模型自适应控制算法目前存在的参数整定和优化困难问题,提出一种改进的粒子群优化算法来优化控制器参数.该算法通过引入基于逻辑斯谛映射产生的混沌算子,并采用线性减小策略调整惯性权重和加速因子,实现对无模型自适应控制参数η、μ、ρ和λ的自动寻优.以典型非线性系统为控制对象进行了仿真.结果 表明,改进的粒子群优化算法具有更...     

基于正态云的粒子群优化算法及其应用

为辨识非线性系统Hammerstein模型,将非线性系统的辨识问题转化为参数空间上的优化问题,提出一种基于正态云模型的改进粒子群算法(NCPSO)。该算法采用动态变异概率,对全局最优粒子和粒子自身最优位置进行正态云变异,以产生新的粒子引导种群的飞行,有效避免早熟收敛。采用一种广义学习策略,提升粒子向最优解飞行的概率,将NCPSO算法用于对Hammerstein模型的辨识,相比其他算法,该算法辨识精度较高。     

应用速度变异粒子群的系统辨识方法研究

论文研究了一种利用粒子群优化（PSO）算法对系统模型进行辨识的新方法。该方法的基本思想是将典型的数学模型的相互组合而构成系统模型的新颖辨识方法,即首先将系统结构辨识问题转化为组合优化问题,然后采用粒子群优化算法同时实现了系统的结构辨识与参数辨识。为了进一步增强粒子群优化算法的辨识性能,提出了一种利用速度变异的粒子群优化（VMPSO）算法。最后,给出了仿真示例,其结果表明了所给的系统辨识方法的合理性和求解算法的有效性。     

基于粒子群优化的Wiener模型辨识与实例研究

针对一类工业过程中可描述成Wiener模型的非线性系统,其辨识问题可等价成以估计参数为优化变量的非线性极小值优化问题．利用粒子群优化(PSO)算法在整个参数空间内并行搜索获得极小值优化问题的最优解(Wiener模型的最优估计),通过对粒子的迭代轨迹进行分析,改进了PSO算法中惯性权重和学习因子的选择．通过一个Wiener模型的数值仿真验证了本文提出的辨识方法的有效性和实用性,并将该方法应用在连续退火机组加热炉产品质量模型的辨识研究,取得了满意的辨识效果．     

蚁群聚类算法的T-S模糊模型辨识

基于T-S模型,提出一种非线性系统的模型辨识方法。利用蚁群聚类算法来进行结构辨识,确定系统的模糊空间和模糊规则数。在聚类的基础上,利用遗传算法辨识模糊模型的后件加权参数,得到一个精确的模糊模型,从而实现参数辨识。仿真结果验证了该方法的有效性,表明该方法能够实现非线性系统的辨识,辨识精度高,可当作复杂系统建模的一种有效手段。     

On-line model identification for the machining process based on multirate process data

Accurate identification of the model parameters of the machining process based on on-line process data is a crucial prerequisite for its model-based control and diagnostics. A typical machining process generates multi-output and multirate data streams. Whereas various sensors provide in-process information about the process, many important process outcomes including product qualities can be only measured in postprocess manner. This paper proposes to improve the identification by using both in-process and postprocess data and by analyzing the identifiability of model parameters. The identification of the model parameters based on multirate output is formulated using the maximum-likelihood estimation and the Fisher information matrix for a multirate-sampled system is derived to study the identifiability of model parameters. A strategy is developed to improve accuracy and robustness of the model identification considering the identifiability. The proposed method is tested on two batches of multirate process data from the cylindrical grinding process. The test results demonstrate using both in-process and postprocess data improves the identifiability and the proposed identification strategy results in improved prediction performance.     

Identification of a modified optimal control model for the human operator

The optimal control model for the human operator has emerged as one of the most promising models for the study of human performance in complex tasks. Previous applications of this model have used heuristic methods based on empirical data to establish numerical values for the model parameters. This was necessary because of the absence of any systematic identification method for the direct extraction of model parameters from experimental data. In this paper, the standard optimal control model is analyzed from the viewpoint of system identification. It is shown that the existing model structure is overparameterized and can be simplified by modifying some of the original assumptions. Identifiability of the resulting modified optimal control model is investigated. As a result, a systematic procedure for the identification of the modified optimal control model parameters from available data is developed. This procedure is validated by application to experimental data from simulation of a piloted tracking task. The paper concludes with recommendations for further simplification in the human operator model structure.     

具有预负载非线性特性的双率系统递推最小二乘估计算法

针对具有预负载非线性特性的双率系统, 提出一种新的辨识方法. 借助切换函数简化系统模型, 通过损失数据模型估计系统损失的输出数据, 进而利用系统所有输入和输出数据, 提出相应双率系统递推最小二乘算法. 与多项式转换方法相比, 该方法能够直接辨识出系统参数. 仿真结果验证了所提出方法的有效性.

     

基于电压递推与FFRELS的航空蓄电池模型参数动态辨识

摘要：针对电池等效电路模型参数具有时变性,难以准确辨识的问题,建立了二阶RC航空电池模型,提出了基于遗忘因子扩展递推最小二乘法(FFRELS)的参数辨识算法;同时针对电池开路电压测量时间长的问题,创建了蓄电池开路电压辨识递推模型,实现开路电压的动态估计,该方法能够准确辨识出模型的参数,有效提高蓄电池开路电压的辨识精度。最后通过仿真实验,验证了该方法能够提高电池模型参数的辨识精度。     

A limit cycle approach for the parametric identification of hysteretic systems

This paper deals with the parametric identification of the Bouc–Wen model for smooth hysteresis. The interest of the problem stems from the fact that the Bouc–Wen model has been used experimentally to represent various hysteretic systems during the last few years. However, identifying the parameters of this model is not a trivial task due to the nonlinearity of the model. In the current literature, most methods devoted to the parametric identification of the Bouc–Wen model rely mainly on numerical simulations, and do not offer, to a large extent, a rigorous proof of the convergence of the estimated parameters to the true ones. This paper presents a new identification method that guarantees the exact determination of the parameters in the absence of perturbations. It is also shown that this method is robust with respect to a class of disturbances of practical interest. The applicability of this method is illustrated by means of experiments.     

一种结合PSO及改进BP网络的辨识方法

当辨识神经网络的类型和结构确定后,初始权值等辨识参数直接影响到辨识效果,而依靠先验知识试凑而得的参数值往往难以达到最佳效果。针对这一问题,提出了一种结合粒子群（PSO）算法及引入动量项的改进BP网络的辨识方法,利用PSO对改进BP网络辨识的初始权值/偏置、学习率、动量系数进行寻优,并将优化后的神经网络模型用在控制系统中进行修正,进一步完善辨识模型。应用在热工系统中,仿真结果表明了该辨识方法的有效性。     

Model parameter estimation of simplified linear models for a continuous paper pulp digester

A physical model of a continuous paper pulp digester is simplified and two subprocesses selected from the digester are modelled by coupled linear partial differential equations. This study focuses on the parameter identification of the simplified linear models. Finite-dimensional approximation of the model is made and a software package developed for identification of distributed parameter processes is applied. This identification system is developed for flexibility to allow identification for different choices of subprocesses and process variables. Unknown parameters of the subprocess models are estimated and the results are illustrated by process simulation and model validation.     

Iterative algorithm for parameter identification of Hammersteinsystems with two-segment nonlinearities

This paper deals with parameter identification of Hammerstein systems having two-segment polynomial nonlinearities. The application of a simple decomposition technique and using switching sequences provide a special form of a Hammerstein model that is linear-in-parameters. This model is used in an iterative algorithm, enabling simultaneous estimation of all of the model parameters. To demonstrate the feasibility of the identification method, more illustrative examples are included     

基于偏差消除最小二乘估计和Durbin方法的两阶段ARMAX参数辨识

针对带有外生变量的自回归移动平均模型(Autoregressive moving average with exogenous variable, ARMAX)的参数辨识问题提出一种两阶段辨识方法. 首先通过偏差消除最小二乘方法辨识带有外生变量的自回归部分(Autoregressive part with exogenous variable, ARX),然后采用Durbin方法将移动平均部分(Moving average, MA)的参数辨识问题转换成一个长自回归模型(Long autoregressive, LAR)的参数辨识问题, 并利用MA与等价LAR的参数对应关系直接得到MA参数, 最后利用辨识出的MA参数计算出噪声方差. 与扩展最小二乘法的数值仿真比较验证了这种两阶段辨识方法的有效性.