初值修正灰色Verhulst模型的参数估计

研究了初值修正项为αz(1)(1)(其中α为修正参数)的灰色Verhuslt模型的修正参数估计方法.针对相关文献中,修正参数α求解无现成公式情况,通过最小化原始序列的一次累加序列与模拟序列之差,建立并求解一个非约束优化模型,获得了初值修正参数α的一个简单有效的计算公式,完善了相关文献中建立的初值修正灰色Verhuslt模型.最后,通过计算实例验证了修正参数公式可以有效提高初值修正灰色Verhuslt模型的精度.     

灰色系统模型病态矩阵的双正则化算法

灰色系统模型矩阵会存在病态问题.为消除其病态性,基于病态矩阵的双正则化方法,建立了正则化灰色系统模型中灰参数求解的表达式,给出了其导出方式;提出了正则参数α的选择原则.从而避免了灰参数求解过程中矩阵的病态问题.数值试验分析说明,灰色系统模型的双正则化算法是正确和适用的.     

改进的GM(1,1)幂模型的构建与应用

在构建GM(1,1)幂模型中,经常利用一阶非齐次线性方程的常数变易法求得GM(1,1)幂模型白化方程的解,再利用白化方程,在灰色系统信息覆盖原理下经过离散化处理推导出参数γ的计算公式,并利用最小二乘法求解参数a,b.但是在求解过程中由于离散化的处理,造成了时间响应预测函数精度的下降。为了弥补精度下降的缺陷,对于预测模型利用PSO算法进行了系数修正.案例对比研究发现,传统的GM(1,1)预测效果最差,改进的GM(1,1)幂模型预测效果最好.     

渗流问题灰色数值模型的解法研究

灰色数值模型的求解是研究灰色数值模型的一个重要问题 .本文根据灰集合、灰数及其灰色运算规则 ,在渗流系统的基本灰色数值模型的基础上 ,分析了求解这类模型的一整套灰色数值算法 ,并对灰色数值算法、普通算法和经典数值方法的计算结果进行了全面比较 ,论证了灰色数值算法对灰信息传递的正确性和对渗流系统描述的合理性 .     

灰色Verhulst模型的改进及其应用

针对灰色Verhulst模型的不足,讨论了灰色Verhulst模型的参数优化问题.首先,利用最小二乘原理给出了一种初值优化的改进模型.其次,在平均相对误差最小准则下,将Verhulst模型的参数优化转化为线性规划问题,然后利用粒子群优化算法估计Verhulst模型中的参数,得到另外一种改进模型.最后,给出了一个仿真实例,结果表明灰色Verhulst模型的改进方法是可行的和有效的,而且具有较高的拟合和预测精度.     

基于初值修正的组合灰色Verhulst模型

对灰色Verhulst模型的拟合精度进行了分析,表明初值的选取对模型精度有重要影响.为提高灰色模型的拟合精度,利用最小二乘原理确定初值中的待定常数,给出了初值修正的灰色Verhulst模型.进一步利用修正模型和传统灰色Verhulst模型建立了组合灰色Verhulst模型,在平均相对误差最小的原则下,利用蚁群算法确定组合权系数.最后通过两个应用实例进行了计算和分析,结果表明,通过初值修正和组合模型能够提高灰色Verhulst模型的拟合精度,便于通过程序实现.     

当绝热指数很大时气体的一种非定常平面平行运动的渐近解

文献[1]在气体的速度分量只与极角θ及时间t有关,而与极距r无关的条件下求解理想气体的非定常平面平行具势运动方程组(1.2)～(1.3).文献[1]指出,在一般的情况下,不能得到解的显式表示式,只是对于二种特殊情况得到了显式解.本文研究了文献[1]的同样问题.第一部分,对音速作了一些补充限制,从而得到了方程组的显式解.第二部分,假设气体的绝热指数γ>>1,求得了方程组的一级近似解.     

变循环发动机模型的求解算法研究

采用部件法建立了变循环发动机的多维非线性隐式方程组模型,模型具有隐式性,因而求解过程复杂,收敛困难.针对上述问题,采用遗传算法和牛顿-拉夫逊法进行模型求解,提出了算法的有效性评价指标:初值敏感性、收敛性和稳定性.对两种算法进行有效性评价.针对发动机工作性能最优化问题,建立了多目标模型,用遗传算法进行求解,并分析了相关变量随马赫数变化的规律.结果可为变循环发动机模型求解算法的选择与设计提供参考.     

关于“改进Huang族算法”一文的注记

J.Flach,在文[1]中对Huang族算法的迭代式这里引进新的参数争φ_k和σ_k,令得到“改进Huang”族算法的迭代公式并在文献[1]的主要定理中证明了这一改进Huang族算法类中,所有算法产生的序列{x_k}只依赖于ρ_k、φ_k和σ_k,与其他参数无关.本文指出,J.Flaohs引进的独立参数仅仅是σ_k,这样[1]的主要结论可改为:改进Huang族算法产生的{x_k}只依赖于ρ_k和σ_k,与其他参数无关.考虑迭代公式     

核范数和谱范数下广义Sylvester方程最小二乘问题的一类改进算法

在文献[10]中,作者从数值角度讨论核范数和谱范数下的广义Sylvester方程约束最小二乘问题min X∈ S|NΣI=1A_iXB_i-C|的算法,其中s为闭凸集合.采用的数值算法是非精确交替方向法,并结合阈值算法、 MoreauYosida正则化算法、谱投影算法、LSQR, SPG等算法求解相应子问题.本文在文献[10]的基础上,通过引入新变量,应用交替方向法简化子问题的求解,其中每个子问题都可以精确求解,更重要的是每个变量都具有显式的表达式.在理论方面我们证明了算法的收敛性,数值试验表明改进后的算法不管是在时间上还是在迭代步上,运行的结果得到很大的改善.     

Analysis of a decoupled time‐stepping algorithm for reduced MHD system modeling magneto‐convection

In this article, we analyze the stability and error estimate of a decoupled algorithm for a magneto‐convection problem. Magneto‐convection is assumed to be modeled by a coupled system of reduced magneto‐hydrodynamic (RMHD) equations and convection‐diffusion equation. The proposed algorithm applies the second‐order backward difference formula in time and finite element in space. To obtain a noniterative decouple algorithm from the fully discrete nonlinear system, we use a second‐order extrapolation in time to the nonlinear terms such that their skew symmetry properties are preserved. We prove the stability of the algorithm and derive error estimates without assuming any stability conditions. The algorithm is unconditionally stable and requires the solution of one RMHD problem and one convection‐diffusion equation per time step. Numerical test is presented that illustrates the accuracy and efficiency of the algorithm.     

Asymptotic expansion of stochastic flows

Summary We study the asymptotic expansion in small time of the solution of a stochastic differential equation. We obtain a universal and explicit formula in terms of Lie brackets and iterated stochastic Stratonovich integrals. This formula contains the results of Doss [6], Sussmann [15], Fliess and Normand-Cyrot [7], Krener and Lobry [10], Yamato [17] and Kunita [11] in the nilpotent case, and extends to general diffusions the representation given by Ben Arous [3] for invariant diffusions on a Lie group. The main tool is an asymptotic expansion for deterministic ordinary differential equations, given by Strichartz [14].     

改进灰导数和时间响应函数的新灰色预测模型

以白化方程为基础,利用梯形公式白化灰导数,同时根据最小二乘法求出时间响应函数中的常数c,得到一种改进的灰色预测模型.应用实例说明改进模型具有较高的预测和拟合精度.     

An Extremal Problem Arising in the Dynamics of Two-Phase Materials That Directly Reveals Information about the Internal Geometry

In two-phase materials, each phase having a non-local response in time, it has been found that for some driving fields the response somehow untangles at specific times, and allows one to directly infer useful information about the geometry of the material, such as the volume fractions of the phases. Motivated by this, and to obtain an algorithm for designing appropriate driving fields, we find approximate, measure independent, linear relations between the values that Markov functions take at a given set of possibly complex points, not belonging to the interval [-1,1] where the measure is supported. The problem is reduced to simply one of polynomial approximation of a given function on the interval [-1,1] and, to simplify the analysis, Chebyshev approximation is used. This allows one to obtain explicit estimates of the error of the approximation, in terms of the number of points and the minimum distance of the points to the interval [-1,1]. Assuming this minimum distance is bounded below by a number greater than 1/2, the error converges exponentially to zero as the number of points is increased. Approximate linear relations are also obtained that incorporate a set of moments of the measure. In the context of the motivating problem, the analysis also yields bounds on the response at any particular time for any driving field, and allows one to estimate the response at a given frequency using an appropriately designed driving field that effectively is turned on only for a fixed interval of time. The approximation extends directly to Markov-type functions with a positive semidefinite operator valued measure, and this has applications to determining the shape of an inclusion in a body from boundary flux measurements at a specific time, when the time-dependent boundary potentials are suitably tailored. © 2022 Wiley Periodicals, Inc.     

Analysis of the symmetric Lanczos algorithm with reorthogonalization methods

We present an error analysis of the symmetric Lanczos algorithm in finite precision arithmetic. The loss of orthogonality among the computed Lanczos vectors is explained with the help of a recurrence formula. A backward error analysis shows that semiorthogonality among the Lanczos vectors is enough to guarantee the accuracy of the computed quantities up to machine precision. The results of this analysis are then extended to the more general case of the Lanczos algorithm with a semiorthogonalization strategy. Based on the recurrence formula, a new reorthogonalization method called partial reorthogonalization is introduced. We show that both partial reorthogonalization and selective orthogonalization as introduced by Parlett and Scott [15] are semiorthogonalization strategies. Finally we discuss the application of our results to the solution of linear systems of equations and to the eigenvalue problem.     

A numerical approach to the infinite horizon problem of deterministic control theory

We are concerned with the Hamilton-Jacobi equation related to the infinite horizon problem of deterministic control theory. Approximate solutions are constructed by means of a discretization in time as well as in the state variable and we prove that their rate of convergence to the viscosity solution is of order 1, provided a semiconcavity assumption is satisfied. A computational algorithm, originally due to R. Gonzales and E. Rofman, is adapted and reformulated for the problem at hand in order to obtain an error estimate for the numerical approximate solutions.This work has been partially supported by CNR-GNAFA.     

A novel multivariate grey model for forecasting the sequence of ternary interval numbers

A novel multivariate grey model suitable for the sequence of ternary interval numbers is presented in the paper. New model takes into account the influencing factors on the system behavior characteristic. New parameter setting makes the model directly applicable to the sequence of ternary interval number without the need to convert the sequence into real sequence. A compensation coefficient taken as a ternary interval number is added to the model equation. The accumulation method based on the new information priority is proposed to estimate coefficients. A connotative prediction formula is derived to replace the white response equation of the classical multivariate grey model. The single variable grey model, which takes into account the development trend of system behavior itself, is combined with the novel multivariate grey model based on the degree of grey incidence. Interval forecasts for China's electricity generation and consumer price index show that the new model has good performance.     

Oscillation and nonoscillation of solutions of second order linear dynamic equations with integrable coefficients on time scales

We obtain Willett-Wong-type oscillation and nonoscillation theorems for second order linear dynamic equations with integrable coefficients on a time scale. The results obtained extend and are motivated by oscillation and nonoscillation results due to Willett [20] and Wong [21] for the second order linear differential equation. As applications of the new results obtained, we give the complete classification of oscillation and nonoscillation for the difference equations

     

Error bounds and estimates for eigenvalues of integral equations

Summary Approximate solutions of the linear integral equation eigenvalue problem can be obtained by the replacement of the integral by a numerical quadrature formula and then collocation to obtain a linear algebraic eigenvalue problem. This method is often called the Nyström method and its convergence was discussed in [7]. In this paper computable error bounds and dominant error terms are derived for the approximation of simple eigenvalues of nonsymmetric kernels.     

The Multichannel Deconvolution Problem: A Discrete Analysis

We study the multichannel deconvolution problem (MDP) in a discrete setting by developing the theory for converting the method used in the continuous setting in [36]. We give a method for solving the MDP when the convolvers are characteristic functions, derive the explicit form of the linear system, and obtain an upper bound on the condition number of the system in a particular case. We compare the Schiske reconstruction [28] to our solution in the discrete setting, and give an explicit formula for the corresponding error. We then give the algorithm for solving the general MDP and discuss in detail the local reconstruction aspects of the problem. Finally, we describe a method for improving the reconstruction by regularization and give some explicit estimates on error bounds in the presence of noise.