互连线高效时域梯形差分模型降阶算法

为了进一步提高现有互连电路模型降阶方法的精度和效率,提出一种基于时域梯形法差分的互连线模型降阶方法.首先将互连电路的时域方程用梯形法差分离散后获得一种关于状态变量的递推关系,形成了一个非齐次Krylov子空间;然后利用非齐次Arnoldi算法求得非齐次Krylov子空间的正交基,再通过正交基对原始系统进行投影得到降阶系统.该算法可以保证时域差分后降阶系统和原始系统的状态变量在离散时间点的匹配,保证时域降阶精度,同时也保证了降阶过程的数值稳定性及降阶系统的无源性.与现有的时域模型降阶方法相比,文中算法可降低计算复杂度;与频域降阶方法相比,由于避免了时频域转换误差,其在时域具有更高的精度.     

求解大型Stein方程的块Krylov子空间方法

本文研究利用块Krylov子空间方法对大型Stein方程降阶求解,分别基于块Arnoldi方法与非对称块Lanczos方法,提出了块Arnoldi Stein方法与非对称块Lanczos Stein方法.数值实验表明提出的方法有效.     

基于重启Lanczos过程的模型降阶方法

针对大规模的线性时不变系统,提出了基于重启Lanczos过程的模型降阶方法。首先,通过重启Lanczos过程分别得到原始系统的可控Gram矩阵的近似矩阵及可观Gram矩阵的近似矩阵。然后,根据原始系统的可控Gram矩阵及可观Gram矩阵所满足的Lyapunov方程构造映射Sylvester方程并求解,对解进行双正交化,得到降阶所需的变换矩阵,从而得到降阶系统。运用此方法对大规模线性时不变系统进行降阶,能够得到具有较高近似精度的稳定的降阶系统。最后,数值算例验证了此方法是行之有效的。     

中心刚体-柔性梁刚柔耦合动力学模型降阶研究

有限单元法被广泛的采用来描述柔性体的弹性变形,然而有限元节点坐标数目庞大,将会给动力学方程求解带来巨大的计算负担.如何降低柔性体的自由度,是当前柔性多体系统动力学研究的一个重要命题.本文以中心刚体-柔性梁系统为例,采用Krylov方法和模态方法进行降价.然后分别采用有限元全模型、Krylov降阶模型和模态降阶模型,对中心刚体-柔性梁进行刚-柔耦合动力学仿真.仿真结果表明,与采用模态降阶方法相比,采用Krylov模型降阶方法只需要较低的自由度,就可以得到与采用有限元方法完全一致的结果.说明Krylov模型降阶方法能够有效的用于柔性多体系统的模型降价研究.     

二次正交Arnoldi方法的隐式重启算法

二次正交Arnoldi方法的存储量小并能保持与标准Arnoldi方法类似的数值稳定性和收敛性,因此成为求解二次特征值问题的重要方法.算法的运行过程中计算量和存储量会不断增加,将算法进行重新启动是算法在实际使用中的必然需求,该方法的特殊分解形式对算法的重启提出了新要求.本文分析了该方法所形成子空间的性质和重启时子空间应具有的形式和性质,提出了一种能够保持算法特殊子空间结构且简便易实现的重启方法.在此基础上分别使用Schur分解、准确位移与精化位移,给出了三种二次正交Arnoldi方法的重启算法.理论分析和数值算例都表明,这些新的重启算法在最大存储量固定的情况下具有很好的可行性与有效性.     

几种模型降阶方法的仿真对比研究

算法比较研究,比较几种主要模型降阶方法的优缺点,为给工程应用提供方法参考.利用奇异值分解的模型降阶方法具有较好的理论性质,能够保持降阶系统结构特性,但计算成本较高故不适合大规模动态系统的降阶;采用矩匹配的模型降阶方法计算简便,适合大规模系统降阶,但无法保证降阶系统稳定性,也很难求得降阶误差界.最小二乘降阶法同时利用了系统的Gramian矩阵和Krylov子空间理论,结合了二者的优点,使得降阶过程计算简化,保持了降阶系统的结构特性,而且降阶误差进一步减小.仿真算例证明了最小二乘法较前两者具有优越性.     

变系数偏微分方程的Galerkin KPOD模型降阶方法

研究了变系数偏微分方程的Galerkin KPOD (Krylov Enhanced Proper Orthogonal De-composition)模型降阶方法.首先基于Galerkin有限元理论建立变系数偏微分方程的空间离散格式,得到具有时变系数矩阵的常微分方程组,并对该常微分方程组应用KPOD模型降阶方法进行降...     

基于隐含重起Arnoldi过程的参数估计

提出在超分辨率复原中使用基于隐含重起Arnoldi过程来高效计算正则化参数的方法。通过隐含重起Arnoldi过程,可选择一个较好的初始向量。该方法将大型稀疏系统矩阵投影到Krylov子空间上并表达成一个小型稠密的Hessenberg矩阵。该方法可减少正则化参数的计算代价。     

Krylov子空间方法及其并行计算

Krylov子空间方法在提高大型科学和工程计算效率上起着重要作用。本文阐述了Krylov子空间方．法产生的背景、Krylov子空间方法的分类,在此基础上,研完了分布式并行计算环境下Krylov子空间方法的并行计算方法,给出了Krylov子空间方法的并行化策略。     

基于ARNOLDI过程的参数估计

本文提出在超分辨率复原中使用基于Amoldi过程来高效计算正则化参数的方法。通过Arnoldi过程分解,该方法将大型稀疏系统矩阵投影到Krylov子空间上并表达成一个小型稠密的Hessenberg矩阵。给出了利用Hessenberg矩阵简化超分辨率复原中解计算的公式。推导了快速计算L曲线的定理。该方法可减少正则化参数的计算代价。     

Multi-order Arnoldi-based model order reduction of second-order time-delay systems

In this paper, we discuss the Krylov subspace-based model order reduction methods of second-order systems with time delays, and present two structure-preserving methods for model order reduction of these second-order systems, which avoid to convert the second-order systems into first-order ones. One method is based on a Krylov subspace by using the Taylor series expansion, the other method is based on the Laguerre series expansion. These two methods are used in the multi-order Arnoldi algorithm to construct the projection matrices. The resulting reduced models can not only preserve the structure of the original systems, but also can match a certain number of approximate moments or Laguerre expansion coefficients. The effectiveness of the proposed methods is demonstrated by two numerical examples.     

Model order reduction of MIMO bilinear systems by multi-order Arnoldi method

In this paper, we present a time domain model order reduction method for multi-input multi-output (MIMO) bilinear systems by general orthogonal polynomials. The proposed method is based on a multi-order Arnoldi algorithm applied to construct the projection matrix. The resulting reduced model can match a desired number of expansion coefficient terms of the original system. The approximate error estimate of the reduced model is given. And we also briefly discuss the stability preservation of the reduced model in some cases. Additionally, in combination with Krylov subspace methods, we propose a two-sided projection method to generate reduced models which capture properties of the original system in the time and frequency domain simultaneously. The effectiveness of the proposed methods is demonstrated by two numerical examples.     

Arnoldi-based model reduction for fractional order linear systems

In this paper, the Arnoldi-based model reduction methods are employed to fractional order linear time-invariant systems. The resulting model has a smaller dimension, while its fractional order is the same as that of the original system. The error and stability of the reduced model are discussed. And to overcome the local convergence of Padé approximation, the multi-point Arnoldi algorithm, which can recursively generate a reduced-order orthonormal basis from the corresponding Krylov subspace, is used. Numerical examples are given to illustrate the accuracy and efficiency of the proposed methods.     

Model-order reductions for MIMO systems using global Krylov subspace methods

This paper presents theoretical foundations of global Krylov subspace methods for model order reductions. This method is an extension of the standard Krylov subspace method for multiple-inputs multiple-outputs (MIMO) systems. By employing the congruence transformation with global Krylov subspaces, both one-sided Arnoldi and two-sided Lanczos oblique projection methods are explored for both single expansion point and multiple expansion points. In order to further reduce the computational complexity for multiple expansion points, adaptive-order multiple points moment matching algorithms, or the so-called rational Krylov space method, are also studied. Two algorithms, including the adaptive-order rational global Arnoldi (AORGA) algorithm and the adaptive-order global Lanczos (AOGL) algorithm, are developed in detail. Simulations of practical dynamical systems will be conducted to illustrate the feasibility and the efficiency of proposed methods.     

解大规模非对称矩阵特征问题的精化Arnoldi方法的一种变形

§1.引言 传统的投影类方法是计算大规模非对称矩阵特征问题Ax=λx部分特征对的主要方法,它们包括Arnoldi方法、块Arnoldi方法、同时迭代法、Davidson方法和Jacobi-Davidson方法,贾提出的精化投影类方法目前被公认为是另一类重要     

Model-order reduction of large-scale second-order MIMO dynamical systems via a block second-order Arnoldi method

In this paper, we present a structure-preserving model-order reduction method for solving large-scale second-order MIMO dynamical systems. It is a projection method based on a block second-order Krylov subspace. We use the block second-order Arnoldi (BSOAR) method to generate an orthonormal basis of the projection subspace. The reduced system preserves the second-order structure of the original system. Some theoretical results are given. Numerical experiments report the effectiveness of this method.     

Structure preserving model reduction of port-Hamiltonian systems by moment matching at infinity

Model reduction of port-Hamiltonian systems by means of the Krylov methods is considered, aiming at port-Hamiltonian structure preservation. It is shown how to employ the Arnoldi method for model reduction in a particular coordinate system in order to preserve not only a specific number of the Markov parameters but also the port-Hamiltonian structure for the reduced order model. Furthermore it is shown how the Lanczos method can be applied in a structure preserving manner to a subclass of port-Hamiltonian systems which is characterized by an algebraic condition. In fact, for the same subclass of port-Hamiltonian systems the Arnoldi method and the Lanczos method turn out to be equivalent in the sense of producing reduced order port-Hamiltonian models with the same transfer function.     

An invert-free Arnoldi method for computing interior eigenpairs of large matrices

This paper presents an invert-free Arnoldi method for extracting a few interior eigenpairs of large sparse matrices. It is derived by implicitly applying the Arnoldi process with the shifted and inverted operator (A?τ I)^?1 in a shifted Krylov subspace (A?τ I)𝒦 _m (A, v ₁). Due to a subtle relationship between the Krylov subspace 𝒦 _m (A, v ₁) and its shifted Krylov subspace, we avoid forming the shifted and inverted operator explicitly. Comparisons are drawn between the harmonic Arnoldi method and the invert-free Arnoldi method. Finally, numerical results are reported to show the efficiency of the new method.     

Model order reduction of nonlinear models based on decoupled multi-model via trajectory piecewise linearization

In this paper a novel model order reduction method for nonlinear models, based on decoupled multi-model, via trajectory piecewise-linearization is proposed. Through different strategies in trajectory piecewise-linear model reduction, model order reduction of a trajectory piecewise-linear model based on output weighting (TPWLOW), has been developed by authors of current work. The structure of mentioned work was founded based on Krylov subspace method, which is appropriate for high order models. Indeed the size of the Krylov subspaces may increase with the number of inputs of the system. As a result, the use of Krylov subspace method may become deficient the case for multi-input systems of order few decades. This paper aims to generalize the idea of model reduction of TPWLOW model by concentrating on balanced truncation technique which is appropriate for medium size systems. In addition, the proposed method either guarantees or provides guaranteed stability in some mentioned conditions. Finally, applicability of the reduced model, instead of high-order decoupled multi-model in weighting multi-model controllers, is investigated through the control of a nonlinear Alstom gasifier benchmark.