一种多输入单输出Hammerstein系统的集成辨识方法

针对多输入单输出(MISO)Hammerstein系统提出了一种稳态与动态辨识相结合的集成辨识方法.该方法利用稳态信息获取稳态模型的强一致性估计,并通过稳态模型以神经网络获得其非线性逼近函数,再利用动态信息辨识获取多输入单输出(MISO)Hammerstein系统的线性子系统未知参数的一致性估计.仿真结果表明了该方法的有效性和实用性.     

Hammerstein模型辨识的回顾及展望

Hammerstein模型是一类具有特定结构的典型非线性模型,由静态非线性环节和动态线性环节串联而成,能较好地反映过程特征的特点,可以描述一大类非线性过程.本文结合Hammerstein模型辨识的基本过程和特点,从Hammerstein模型中间变量不可测量的角度出发,首先按静态非线性环节与动态线性环节同步辨识法和分步辨识法综述了Hammerstein模型的相关理论和方法;然后,分析了现有的基于Hammerstein模型的控制系统设计方案;最后对Hammerstein系统未来可能的研究提出若干看法.     

含有色噪声的神经模糊Hammerstein模型分离辨识

针对实际工业过程中普遍存在的有色噪声,本文提出一种基于递推增广最小二乘算法的神经模糊Hammerstein模型辨识方法,突破了传统的Hammerstein模型迭代分离算法.首先,利用多信号源实现Hammerstein模型中静态非线性环节和动态线性环节的分离,大大简化了辨识过程,提高了串联环节参数的分离精度.其次,利用长除法将噪声模型用有限脉冲响应模型逼近,采用增广递推最小二乘法进行线性环节的参数估计.最后,采用神经模糊模型拟合静态非线性环节,同时设计了神经模糊模型参数的非迭代优化算法,改善了模型的使用范围.该方法保证了模型的预测精度,对含有色噪声的非线性系统具有较好的拟合效果.仿真结果验证了上述方法的有效性.     

质子交换膜燃料电池的分数阶非线性状态空间模型研究

针对质子交换膜燃料电池(PEMFC)发电过程中的分数阶和非线性特性,本文提出了一种分数阶子空间辨识方法建立了PEMFC非线性状态空间模型.首先,为了降低建模复杂度,采用典型相关分析法和相关分析法确定了模型输入变量;其次,将分数阶微分理论与Hammerstein模型子空间辨识方法相结合,采用Poisson矩函数对输入输出数据进行预处理,构造了子空间辨识方法的输入输出矩阵,并引入分数阶短时记忆法减少辨识算法计算量;最后,选取多项式作为Hammerstein模型前端静态非线性环节,采用模糊遗传算法优化系统分数阶阶次和系数矩阵.仿真结果验证了算法的有效性,改进的辨识算法可以明显减小计算时间,所得PEMFC辨识模型能够准确地描述PEMFC的动态过程.     

基于Hammerstein-Wiener模型的CSTR反应器辨识

针对化工过程中广泛应用的连续搅拌反应釜（CSTR）反应器，提出一种新的基于极限学习机的Hammerstein-Wiener模型的辨识建模方法。其中，Hammerstein-Wiener模型的两个非线性环节采用两个不同的极限学习机逼近，线性环节采用自回归ARX模型。因极限学习机的特殊结构，此模型可以表示成线性回归的形式，最终利用广义最小二乘法求解模型的参数。此方法辨识过程简单，辨识过程的计算量较小。最后对CSTR的辨识结果表明，在相同条件下与基于多项式的Hammerstein模型和ARX-LSSVM Hammerstein模型相比，该方法具有较高辨识精度，表明了该方法的有效性。     

基于SVR的传感器Hammerstein模型辨识

提出一种基于支持向量回归机的非线性动态传感器Hammerstein模型辨识方法并给出了相关的数学理论及学习算法.在该模型中,用非线性静态子环节和线性动态子环节串联来描述传感器的非线性动态特性.再利用函数展开将模型的非线性传递函数转换为等价的线性中间模型,并通过SVR求取中间模型参数.最后,推导出中间模型参数与传感器Hammerstein模型参数之间的关系,并由该关系实现非线性静态环节和线性动态环节的同时辨识.用实际力传感器动态标定实验数据进行测试,结果表明与常规非线性传感器辨识方法不同,所提方法只需进行一次动态标定实验就能给出非线性动态模型的数学解析表达式.且建立的力传感器Hammerstein模型阶次为4,而线性动态系统模型则需要6阶才能达到相同的精度.因此该研究为传感器非线性动态系统辨识又提供了一种可选方法.     

风洞系统非线性块状结构模型

本文将非线性块状模型的建模思想引入风洞系统模型的建立过程中,针对主排气阀和栅指电液伺服机构具有死区非线性特性,分别用含有死区输入的Hammerstein块状模型描述其动态特性,将主排气阀和栅指机构的输出作为风洞流场的输入,建立两输入两输出多变量耦合动态模型.两个独立的Hammerstein子模型与线性动态耦合的风洞流场模型串联构成一个非线性多变量块状模型.采用自适应加权递推辨识算法在线辨识Hammerstein子模型参数,采用带有遗忘因子的递推最小二乘法辨识风洞流场模型参数.仿真与风洞现场测试结果验证了本文方法的有效性.     

应用遗传算法辨识Hammerstein模型

基于遗传算法,提出了一种辨识Hammerstein模型的方法,该方法能够克服有色观测噪声的污染,获得非线性静态环节参数和线性动态环节参数的无偏估计,并与Hammerstein模型的MSLS辨识方法进行了比较,仿真结果说明了该方法的有效性。     

基于Hammerstein型神经网络的非线性动态系统辨识

Hammerstein模型广泛应用于非线性系统的辨识中,其结构是由非线性静态增益部分和一个线性动态部分串联。提出一种Hammerstein型神经网络用来模拟传统的Hammerstein模型,并将其应用于非线性动态系统的辨识中。由Lipschitz熵来确定Hammerstein型神经网络的阶次,并利用反向传播算法对网络权值的进行训练。仿真结果表明,Hammerstein型神经网络具有较好的非线性动态系统辨识性能。     

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.     

New identification method for Hammerstein models based on approximate least absolute deviation

Disorder and peak noises or large disturbances can deteriorate the identification effects of Hammerstein non-linear models when using the least-square (LS) method. The least absolute deviation technique can be used to resolve this problem; however, its absolute value cannot meet the need of differentiability required by most algorithms. To improve robustness and resolve the non-differentiable problem, an approximate least absolute deviation (ALAD) objective function is established by introducing a deterministic function that exhibits the characteristics of absolute value under certain situations. A new identification method for Hammerstein models based on ALAD is thus developed in this paper. The basic idea of this method is to apply the stochastic approximation theory in the process of deriving the recursive equations. After identifying the parameter matrix of the Hammerstein model via the new algorithm, the product terms in the matrix are separated by calculating the average values. Finally, algorithm convergence is proven by applying the ordinary differential equation method. The proposed algorithm has a better robustness as compared to other LS methods, particularly when abnormal points exist in the measured data. Furthermore, the proposed algorithm is easier to apply and converges faster. The simulation results demonstrate the efficacy of the proposed algorithm.     

Optimal input design for set-membership identification of Hammerstein models

In this paper the problem of optimal input design for the identification of Hammerstein models is considered under the assumption that the linear dynamic part of the model is a FIR and that lower and upper bounds are available for the additive measurement errors. The parameters of the Hammerstein model can then be estimated via the identification of a linearized augmented Hammerstein model . External approximations of the feasible intervals for the parameters of the original Hammerstein models are then derived (which may correspond to the actual feasible intervals). This paper deals with the design of input sequences minimizing parameter uncertainty for the linearized augmented Hammerstein model . Some new results are also reported about optimal input design for polynomial non-linear blocks, that may be part of Hammerstein models.     

Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises

A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method.     

On identification of Hammerstein and Wiener model with application to virtualised software system

This paper proposes a system identification method for estimating virtualised software system dynamics within the framework of a Hammerstein–Wiener model. Building on the authors’ previous work in identification and control of the software systems, the approach utilises frequency sampling filter structure to describe the linear dynamics and B-spline curve functions for the inverse static output nonlinearity. Furthermore, the issue on parameter selection for B-spline model approximation of scatter data is addressed by using a data clustering method. An experimental test-bed of virtualised software system is established to generate real observational data which are used to confirm the performance of the proposed approach. The identification results have shown that the model efficacy is increased with the proposed approach because the dimension of the nonlinear model can be significantly reduced while maintaining the desired accuracy.     

Recursive identification of Hammerstein systems with discontinuous nonlinearities containing dead-zones

This note deals with the recursive parameter identification of Hammerstein systems with discontinuous nonlinearities, i.e., two-segment piecewise-linear with dead-zones and preloads. A special form of the Hammerstein model with this type of nonlinearity is incorporated into the recursive least squares identification scheme supplemented with the estimation of model internal variables. The proposed method is illustrated by examples.     

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     

Modeling and parameter identification of systems with multisegmentpiecewise-linear characteristics

This paper deals with the modeling and parameter identification of nonlinear systems having multi-segment piecewise-linear characteristics. The decomposition of the corresponding mapping provides a new form of multi-segment nonlinearity representation, leading to an output equation where all the parameters to be estimated are separated. Hence, an iterative method with internal variable estimation can be applied for parameter identification using input/output data records. The only required a-priori knowledge of the nonlinear characteristic represents the limits for the domain partition. The proposed model of given static nonlinearity is also incorporated into the Hammerstein model. Examples of parameter identification for static and dynamic systems with multi-segment piecewise-linear characteristics are presented     

一类双线性Hammerstein模型的集成辨识方法

对于一类静态非线性增益具有原点对称特性的双线性Ｈａｍｍｅｒｓｔｅｉｎ模型,提出了一种稳态与动态辨识相结合的集成辨识方法。该方法利用稳态信号获得稳态模型的强一致性估计,并通过稳态模型获得非线性增益的估计,再利用动态信息辨识获得双线性Ｈａｍｍｅｒｉｓｔｅｉｎ模型的双线性系统未知参数的一致性估计。仿真结果表明了该方法的有效性和实用性。