共查询到19条相似文献,搜索用时 203 毫秒
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针对一类非均匀数据采样Hammerstein-Wiener 系统, 提出一种递阶多新息随机梯度算法. 首先基于提升技术, 推导出系统的状态空间模型, 并考虑因果约束关系, 将该模型分解成两个子系统, 利用多新息遗忘随机梯度算法辨识出此模型的参数; 然后, 引入可变遗忘因子, 提出一种修正函数并在线确定其大小, 提高了算法的收敛速度及抗干扰能力. 仿真实例验证了所提出算法的有效性和优越性.
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基于SVR的传感器Hammerstein模型辨识 总被引:1,自引:0,他引:1
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. 相似文献
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提出了一种基于高斯核函数的Hammerstein非线性系统参数辨识方法。Hammerstein非线性系统由一个静态非线性模块和一个动态线性模块串联组成,利用高斯核函数神经网络和传递函数模型分别建立Hammerstein系统的静态非线性模块和动态线性模块。首先,基于可分离信号的输入输出数据,采用相关性分析方法估计动态线性模块的参数,有效抑制噪声的干扰。其次,针对Hammerstein非线性系统的不可测噪声项,利用残差的估计值代替不可测变量,推导了递推增广最小二乘辨识方法,根据随机信号的输入输出数据辨识静态非线性模块和噪声模型的参数。仿真结果表明,针对有色噪声干扰的Hammerstein非线性系统,所提方法具有较好的辨识精度和鲁棒性。 相似文献
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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. 相似文献
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提出一种基于支持向量回归机(SVR)的非线性动态系统建模方法。用非线性静态子环节和线性动态子环节串联——Hammerstein模型来描述非线性动态系统。然后,通过函数展开将Hammerstein模型的非线性传递函数转换为等价的线性形式,从而建立起线性中间模型。再由SVR算法辨识出中间模型参数。最后,通过中间模型参数与Hammerstein模型参数之间的关系,实现原系统的非线性静态环节和线性动态环节的同时辨识。用非线性动态系统标定实验数据进行测试,建模结果表明所提方法具有如下优点:1)只需进行一次动态标定实验; 2)能给出非线性动态模型的数学解析表达式;3)充分利用SVR的优点,使所建模型具有更好的鲁棒性。该研究为非线性动态系统建模又提供了一种新方法。 相似文献
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一些工业过程可以近似用一个传递函数描述,结合统计辨识方法和非线性优化策略提出传递函数参数辨识方法.该方法采用动态数据方案,使用系统观测数据获得系统更多的模态信息.基于动态观测数据,提出传递函数随机梯度参数辨识方法.为进一步提高辨识精度,利用动态窗数据将随机梯度参数辨识方法中的标量新息扩展为新息向量,提出传递函数多新息随机梯度参数估计方法.最后通过仿真例子对所提出的方法进行了性能分析和模型验证. 相似文献
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基于辅助模型的量化控制系统辨识方法 总被引:1,自引:1,他引:0
针对具有通信约束的量化控制系统模型, 在采用随机重复性试验测量信息的技术上, 提出了基于辅助模型的量化系统参数辨识方法. 首先分析了在随机重复性试验方法下量化系统的模型特征并给出了分两步辨识的策略.分析表明, 在上述模型里系统具有时变的估计误差, 推导了进行参数辨识所满足的持续激励条件, 并给出了基于辅助模型的多新息量化辨识递推算法. 接着研究了所给出辨识算法的收敛性分析, 得到了系统参数估计误差上界的计算式,最后将方法推广到一类Hammerstein非线性系统量化辨识问题上. 数字仿真验证了该算法及结论 相似文献
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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. 相似文献
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The identification of nonlinear systems is a hot topic in the identification fields. In this paper, a data filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein nonlinear controlled autoregressive moving average systems by adopting the key-term separation principle and the data filtering technique. The proposed algorithm provides a reference to improve the identification accuracy of the nonlinear systems with colored noise. The simulation results show that the new algorithm can more effectively estimate the parameters of the Hammerstein nonlinear systems than the multi-innovation stochastic gradient algorithm. 相似文献
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Parameter estimation for nonlinear systems by using the data filtering and the multi-innovation identification theory 总被引:1,自引:0,他引:1
Yawen Mao 《国际计算机数学杂志》2016,93(11):1869-1885
For Hammerstein output-error autoregressive systems, a decomposition based multi-innovation stochastic gradient (D-MISG) identification algorithm and a data filtering based multi-innovation stochastic gradient (F-MISG) identification algorithm are derived by means of the key-term separation principle and the multi-innovation identification theory. The D-MISG algorithm uses the decomposition technique to transform a Hammerstein system into two subsystems and requires less computational cost, and the F-MISG algorithm uses a linear filter to filter the input-output data and has a higher estimation accuracy for larger innovation lengths. The simulation results show that the proposed two algorithm can give satisfactory parameter estimates. 相似文献
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This paper considers the identification problem for Hammerstein output error moving average (OEMA) systems. An auxiliary model-based recursive extended least-squares (RELS) algorithm and an auxiliary model-based multi-innovation extended least-squares (MI-ELS) algorithm are presented using the multi-innovation identification theory. The basic idea is to express the system output as a linear combination of the parameters by using the key-term separation principle and auxiliary model method. The proposed algorithms can give highly accurate parameter estimates. The simulation results show the effectiveness of the proposed algorithms. 相似文献
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Data Filtering Based Multi-innovation Gradient Identification Methods for Feedback Nonlinear Systems
Bingbing Shen Feng Ding Ling Xu Tasawar Hayat 《International Journal of Control, Automation and Systems》2018,16(5):2225-2234
With the development of industry information technology, many researchers pay attention to the estimation problems of feedback nonlinear systems increasingly. In this paper, a filtering based multi-innovation stochastic gradient algorithm is derived for Hammerstein equation-error autoregressive systems by using the hierarchical technique. The parameter estimates accuracy can be improved with the innovation length increasing. These algorithms are easy to implement on-line. The simulation results verify the effectiveness of the proposed algorithm. 相似文献
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It is well-known that the stochastic gradient (SG) identification algorithm has poor convergence rate. In order to improve the convergence rate, we extend the SG algorithm from the viewpoint of innovation modification and present multi-innovation gradient type identification algorithms, including a multi-innovation stochastic gradient (MISG) algorithm and a multi-innovation forgetting gradient (MIFG) algorithm. Because the multi-innovation gradient type algorithms use not only the current data but also the past data at each iteration, parameter estimation accuracy can be improved. Finally, the performance analysis and simulation results show that the proposed MISG and MIFG algorithms have faster convergence rates and better tracking performance than their corresponding SG algorithms. 相似文献
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Feng Ding 《Digital Signal Processing》2010,20(4):1027-1039
This paper considers connections between the cost functions of some parameter identification methods for system modelling, including the well known projection algorithm, stochastic gradient (SG) algorithm and recursive least squares (RLS) algorithm, and presents a modified SG algorithm by introducing the convergence index and a multi-innovation projection algorithm, a multi-innovation SG algorithm and a multi-innovation RLS algorithm by introducing the innovation length, aiming at improving the convergence rate of the SG and RLS algorithms. Furthermore, this paper derives an interval-varying multi-innovation SG and an interval-varying multi-innovation RLS algorithm in order to deal with missing data cases. 相似文献
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Qinyao Liu Feng Ding Ahmed Alsaedi Tasawar Hayat 《International Journal of Control, Automation and Systems》2018,16(3):1070-1079
This paper studies the parameter identification problems of multivariate output-error moving average systems. An auxiliary model based extended stochastic gradient algorithm and based recursive extended least squares algorithm are proposed for estimating the parameters of the multivariate output-error moving average systems. By using the multi-innovation identification theory, an auxiliary model based multi-innovation extended stochastic gradient algorithm is derived for improving the parameter estimation accuracy. Finally, the simulation results indicate that the proposed algorithms can work well. 相似文献