参数不确定非自治混沌系统的自适应指数同步

针对参数不确定非自治混沌系统,研究了指数同步问题。给出了自适应控制器的构造方法,并运用Lyapunov稳定性定理证明了在该控制器下的误差系统是指数稳定的,且可以通过调整控制参数控制同步时间。最后,利用MATLAB软件对两个含有不确定参数的非自治混沌系统进行了数值仿真,验证了所提出方法的有效性和正确性.     

具有不确定参数二次自治系统的自适应同步问题研究

研究了两个带有参数的n维二次自治系统的自适应同步问题.根据Lyapunov稳定性理论及自适应控制方法得到了两个带有不确定参数非线性系统状态的渐近同步自适应控制方法.同时,识别了不确定参数.通过对两个超混沌系统的理论研究,证明了给定的非线性动力系统同步策略和参数识别的可行性.     

超混沌系统的混合同步控制

基于Lyapunov稳定性理论,提出了一种超混沌系统混合同步控制方法,给出并详细证明了Rossler超混沌系统实现自同步的充分条件以及控制律参数的取值范围,并构建了两个不同结构的Rossler超混沌系统的异结构快速同步的数学模型。数值仿真表明了所设控制器的有效性和方法的可操作性．     

不确定时滞系统的时滞相关非脆弱鲁棒H∞控制

讨论了不确定时滞系统非脆弱控制器设计问题.利用Lyapunov-Krasovskii稳定性理论和最近建立的积分不等式方法,获得了不确定时滞系统在非脆弱控制器作用下不仅内部渐近稳定,而且具有给定的H∞扰动抑制水平γ的时滞相关条件.然后,针对控制器具有加法不确定性和乘法不确定性两种情况,分别给出了非脆弱控制器的设计方法,这一方法不需要调节参数,利用Matlab的LMI工具箱求解方便,数值仿真实例说明该方法的有效性.     

不确定时滞系统的时滞相关非脆弱鲁棒[[H_infty]]控制

讨论了不确定时滞系统非脆弱控制器设计问题.利用Lyapunov-Krasovskii稳定性理论和最近建立的积分不等式方法,获得了不确定时滞系统在非脆弱控制器作用下不仅内部渐近稳定,而且具有给定的H∞扰动抑制水平γ的时滞相关条件.然后,针对控制器具有加法不确定性和乘法不确定性两种情况,分别给出了非脆弱控制器的设计方法,这一方法不需要调节参数,利用Matlab的LMI工具箱求解方便,数值仿真实例说明该方法的有效性.     

政府调控下的能源供需系统动力学分析

依据一个经济时期内能源需求、能源供应和政府调控之间相互依存、相互制约的演化关系为背景建立了一个新的能源供需模型.模型中引入市场自身调节的闽值,政府调控的阈值,政府调控对能源需求和能源供应的影响系数等参数,通过参数的调整,分析了政府调控在能源供需中的作用.通过平衡点稳定性、系统的耗散性、Lyapunov指数谱等的分析,研究了系统的基本动力学行为,利用数值模拟的方法给出了系统的动力演化行为;给出了模型中参数估计的方式,对模型所反映的现实意义进行了解释,给出了数值模拟结果,验证了理论分析的正确性.     

一个非线性电力系统的混沌振荡

分析了一个非线性三参数电力系统振荡的异宿分枝,给出Melnikov函数的留数计算法,并获得电力系统发生混沌振荡的锥性参数区域和带形参数区域,为大偏差状态下保障电力系统稳定运行提供了理论依据和计算方法.     

一类非线性时滞混沌系统的自适应脉冲同步

针对一类非线性时滞混沌系统,提出了一种新的自适应脉冲同步方案.首先基于Lyapunov稳定性理论、自适应控制理论及脉冲控制理论设计了自适应控制器、脉冲控制器及参数自适应律,然后利用推广的Barbalat引理,理论证明响应系统与驱动系统全局渐近同步,并给出了相应的充分条件.方案利用参数逼近Lipschitz常数,从而取消了Lipschitz常数已知的假设.两个数值仿真例子表明本方法的有效性.     

OGY方法的改进及证明*

OGY方法是混沌控制最重要的方法.通过选取系统参数的小变化,使双曲不动点变“稳定”.本文改进了OGY方法中的参数选取方法,并且完成了对OGY方法的严格证明.     

永磁同步电动机中的混沌现象

讨论永磁同步电动机（PMSM）的动态特性,给出常输入电压、常外部扭转条件下的系统稳态特性表达式,基于Hopf分支条件提出一种调节系统参数的方法,以使其呈现极限环或混沌行为。计算机仿真结果表明在永磁同步电动机中存在混沌现象。     

Finding roots of a real polynomial simultaneously by means of Bairstow's method

Aberth's method for finding the roots of a polynomial was shown to be robust. However, complex arithmetic is needed in this method even if the polynomial is real, because it starts with complex initial approximations. A novel method is proposed for real polynomials that does not require any complex arithmetic within iterations. It is based on the observation that Aberth's method is a systematic use of Newton's method. The analogous technique is then applied to Bairstow's procedure in the proposed method. As a result, the method needs half the computations per iteration than Aberth's method. Numerical experiments showed that the new method exhibited a competitive overall performance for the test polynomials.     

A NEW STEPSIZE FOR THE STEEPEST DESCENT METHOD

The steepest descent method is the simplest gradient method for optimization. It is well known that exact line searches along each steepest descent direction may converge very slowly. An important result was given by Barzilar and Borwein, which is proved to be superlinearly convergent for convex quadratic in two dimensional space, and performs quite well for high dimensional problems. The BB method is not monotone, thus it is not easy to be generalized for general nonlinear functions unless certain non-monotone techniques being applied. Therefore, it is very desirable to find stepsize formulae which enable fast convergence and possess the monotone property. Such a stepsize αk for the steepest descent method is suggested in this paper. An algorithm with this new stepsize in even iterations and exact line search in odd iterations is proposed. Numerical results are presented, which confirm that the new method can find the exact solution within 3 iteration for two dimensional problems. The new method is very efficient for small scale problems. A modified version of the new method is also presented, where the new technique for selecting the stepsize is used after every two exact line searches. The modified algorithm is comparable to the Barzilar-Borwein method for large scale problems and better for small scale problems.     

函数方程求根的一种新型大范围收敛迭代法

1引言实际解函数方程f(x)=0(z∈[α,β],f(α)f(β)＜0,f(x~*)=0)时,人们常常希望选用那些仅计算函数值,具有大范围收敛性且效率较高的方法,特别对那些表示式复杂的函数以及病态函数．例如,那种仅在的某个充分小邻域内连续,而在该邻域之外光滑性很差的函数；那种在初始含根区间(α,β)上起伏多变的函数；那种|f(α)|和|f(β)|差别甚大,而x~*又十分靠近绝对值较大者一端的函数等等,这种欲望就更加强烈．     

Practical acceleration for computing the HITS ExpertRank vectors

A meaningful rank as well as efficient methods for computing such a rank are necessary in many areas of applications. Major methodologies for ranking often exploit principal eigenvectors. Kleinberg’s HITS model is one of such methodologies. The standard approach for computing the HITS rank is the power method. Unlike the PageRank calculations where many acceleration schemes have been proposed, relatively few works on accelerating HITS rank calculation exist. This is mainly because the power method often works quite well in the HITS setting. However, there are cases where the power method is ineffective, moreover, a systematic acceleration over the power method is desirable even when the power method works well. We propose a practical acceleration scheme for HITS rank calculations based on the filtered power method by adaptive Chebyshev polynomials. For cases where the gap-ratio is below 0.85 for which the power method works well, our scheme is about twice faster than the power method. For cases where gap-ratio is unfavorable for the power method, our scheme can provide significant speedup. When the ranking problems are of very large scale, even a single matrix–vector product can be expensive, for which accelerations are highly necessary. The scheme we propose is desirable in that it provides consistent reduction in number of matrix–vector products as well as CPU time over the power method, with little memory overhead.     

Accelerating the convergence in the single-source and multi-source Weber problems

The modified Weiszfeld method [Y. Vardi, C.H. Zhang, A modified Weiszfeld algorithm for the Fermat-Weber location problem, Mathematical Programming 90 (2001) 559-566] is perhaps the most widely-used algorithm for the single-source Weber problem (SWP). In this paper, in order to accelerate the efficiency for solving SWP, a new numerical method, called Weiszfeld-Newton method, is developed by combining the modified Weiszfeld method with the well-known Newton method. Global convergence of the new Weiszfeld-Newton method is proved under mild assumptions. For the multi-source Weber problem (MWP), a new location-allocation heuristic, Cooper-Weiszfeld-Newton method, is presented in the spirit of Cooper algorithm [L. Cooper, Heuristic methods for location-allocation problems, SIAM Review 6 (1964) 37-53], using the new Weiszfeld-Newton method in the location phase to locate facilities and adopting the nearest center reclassification algorithm (NCRA) in the allocation phase to allocate the customers. Preliminary numerical results are reported to verify the evident effectiveness of Weiszfeld-Newton method for SWP and Cooper-Weiszfeld-Newton method for MWP.     

Fourier-Chebyshev spectral method for cavitation computation in nonlinear elasticity

A Fourier-Chebyshev spectral method is proposed in this paper for solving the cavitation problem in nonlinear elasticity. The interpolation error for the cavitation solution is analyzed, the elastic energy error estimate for the discrete cavitation solution is obtained, and the convergence of the method is proved. An algorithm combined a gradient type method with a damped quasi-Newton method is applied to solve the discretized nonlinear equilibrium equations. Numerical experiments show that the Fourier-Chebyshev spectral method is efficient and capable of producing accurate numerical cavitation solutions.     

求解病态线性方程组的收缩Lanczos方法

Lanczos方法是求解大型线性方程组的常用方法.遗憾的是,在Lanczos过程中通常会发生算法中断或数值不稳定的情况.将给出求解大型对称线性方程组的收缩Lanczos方法,即DLanczos方法.新算法将采用增广子空间技术,在Lanczos过程中向Krylov子空间加入少量绝对值较小的特征值所对应的特征向量进行收缩.数值实验表明,新算法比Lanczos方法收敛速度更快,并且适合求解病态对称线性方程组.     

A Comparison Study of Deep Galerkin Method and Deep Ritz Method for Elliptic Problems with Different Boundary Conditions

Recent years have witnessed growing interests in solving partial differential equations by deep neural networks, especially in the high-dimensional case. Unlike classical numerical methods, such as finite difference method and finite element method, the enforcement of boundary conditions in deep neural networks is highly nontrivial. One general strategy is to use the penalty method. In the work, we conduct a comparison study for elliptic problems with four different boundary conditions, i.e., Dirichlet, Neumann, Robin, and periodic boundary conditions, using two representative methods: deep Galerkin method and deep Ritz method. In the former, the PDE residual is minimized in the least-squares sense while the corresponding variational problem is minimized in the latter. Therefore, it is reasonably expected that deep Galerkin method works better for smooth solutions while deep Ritz method works better for low-regularity solutions. However, by a number of examples, we observe that deep Ritz method can outperform deep Galerkin method with a clear dependence of dimensionality even for smooth solutions and deep Galerkin method can also outperform deep Ritz method for low-regularity solutions.Besides, in some cases, when the boundary condition can be implemented in an exact manner, we find that such a strategy not only provides a better approximate solution but also facilitates the training process.     

GL method for solving equations in math-physics and engineering

In this paper, we propose a GL method for solving the ordinary and the partial differential equation in mathematical physics and chemics and engineering. These equations govern the acustic, heat, electromagnetic, elastic, plastic, flow, and quantum etc. macro and micro wave field in time domain and frequency domain. The space domain of the differential equation is infinite domain which includes a finite inhomogeneous domain. The inhomogeneous domain is divided into finite sub domains. We present the solution of the differential equation as an explicit recursive sum of the integrals in the inhomogeneous sub domains. Actualy, we propose an explicit representation of the inhomogeneous parameter nonlinear inversion. The analytical solution of the equation in the infinite homogeneous domain is called as an initial global field. The global field is updated by local scattering field successively subdomaln by subdomain. Once all subdomains are scattered and the updating process is finished in all the sub domains, the solution of the equation is obtained. We call our method as Global and Local field method, in short , GL method. It is different from FEM method, the GL method directly assemble inverse matrix and gets solution. There is no big matrix equation needs to solve in the GL method. There is no needed artificial boundary and no absorption boundary condition for infinite domain in the GL method. We proved several theorems on relationships between the field solution and Green＇s function that is the theoretical base of our GL method. The numerical discretization of the GL method is presented. We proved that the numerical solution of the GL method convergence to the exact solution when the size of the sub domain is going to zero. The error estimation of the GL method for solving wave equation is presented. The simulations show that the GL method is accurate, fast, and stable for solving elliptic, parabolic, and hyperbolic equations. The GL method has advantages and wide applications in the 3D electromagnetic （EM）     

AN ADAPTIVE MULTI-SCALE CONJUGATE GRADIENT METHOD FOR DISTRIBUTED PARAMETER ESTIMATION OF 2-D WAVE EQUATION

An adaptive multi-scale conjugate gradient method for distributed parameter estimations (or inverse problems) of wave equation is presented. The identification of the coefficients of wave equations in two dimensions is considered. First, the conjugate gradient method for optimization is adopted to solve the inverse problems. Second, the idea of multi-scale inversion and the necessary conditions that the optimal solution should be the fixed point of multi-scale inversion method is considered. An adaptive multi-scale inversion method for the inoerse problem is developed in conjunction with the conjugate gradient method. Finally, some numerical results are shown to indicate the robustness and effectiveness of our method.