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11.
Chu-Li Fu Xiang-Tuan Xiong Zhi Qian 《Journal of Mathematical Analysis and Applications》2007,331(1):472-480
In this paper a simple and convenient new regularization method for solving backward heat equation—Fourier regularization method is given. Meanwhile, some quite sharp error estimates between the approximate solution and exact solution are provided. A numerical example also shows that the method works effectively. 相似文献
12.
神经网络集成技术能有效地提高神经网络的预测精度和泛化能力,已经成为机器学习和神经计算领域的一个研究热点.利用Bagging技术和不同的神经网络算法生成集成个体,并用偏最小二乘回归方法从中提取集成因子,再利用贝叶斯正则化神经网络对其集成,以此建立上证指数预测模型.通过上证指数开、收盘价进行实例分析,计算结果表明该方法预测精度高、稳定性好. 相似文献
13.
V. A. Morozov E. M. Mukhamadiev A. B. Nazimov 《Computational Mathematics and Mathematical Physics》2007,47(12):1885-1892
Regularization of singular systems of linear algebraic equations by shifts is examined. New equivalent conditions for the shift regularizability of such systems are derived. 相似文献
14.
15.
Frozen Landweber Iteration for Nonlinear Ill-Posed Problems 总被引:1,自引:0,他引:1
J. Xu B. Han L. Li 《应用数学学报(英文版)》2007,23(2):329-336
In this paper we propose a modification of the Landweber iteration termed frozen Landweberiteration for nonlinear ill-posed problems.A convergence analysis for this iteration is presented.The numericalperformance of this frozen Landweber iteration for a nonlinear Hammerstein integral equation is compared withthat of the Landweber iteration.We obtain a shorter running time of the frozen Landweber iteration based onthe same convergence accuracy. 相似文献
16.
An inverse problem of the reconstruction of the right-hand side of the Euler-Darboux equation is studied. This problem is equivalent to the Volterra integral equation of the third kind with the operator of multiplication by a smooth nonincreasing function. Numerical solution of this problem is constructed using an integral representation of the solution of the inverse problem, the regularization method, and the method of quadratures. The convergence and stability of the numerical method is proved. 相似文献
17.
In this paper, we discuss the classical ill-posed problem of numerical differentiation, assuming that the smoothness of the function to be differentiated is unknown. Using recent results on adaptive regularization of general ill-posed problems, we propose new rules for the choice of the stepsize in the finite-difference methods, and for the regularization parameter choice in numerical differentiation regularized by the iterated Tikhonov method. These methods are shown to be effective for the differentiation of noisy functions, and the order-optimal convergence results for them are proved.
18.
The paper outlines a procedure to identify the space-and time-dependent external nonstationary load acting on a closed circular
cylindrical shell of medium thickness. Time-dependent deflections at several points of the shell are used as input data to
solve the inverse problem. Examples of numerical identification of various nonstationary loads, including moving ones are
presented. The relationship between the external load and the stress-strain state of the shell is described by the Volterra
equation of the first kind. The identification problem is solved using Tikhonov's regularization method and Apartsin's h-regularization method
__________
Translated from Prikladnaya Mekhanika, Vol. 44, No. 7, pp. 91–100, July 2008. 相似文献
19.
对于双方带扰动数据的病态方程(即所谓广义病态方程),借助对Tikhonov正则化算法的改进,给出一种优良的正则化求解方法。 相似文献
20.
Convergent Algorithm Based on Progressive Regularization for Solving Pseudomonotone Variational Inequalities 总被引:2,自引:2,他引:0
In this paper, we extend the Moreau-Yosida regularization of monotone variational inequalities to the case of weakly monotone and pseudomonotone operators. With these properties, the regularized operator satisfies the pseudo-Dunn property with respect to any solution of the variational inequality problem. As a consequence, the regularized version of the auxiliary problem algorithm converges. In this case, when the operator involved in the variational inequality problem is Lipschitz continuous (a property stronger than weak monotonicity) and pseudomonotone, we prove the convergence of the progressive regularization introduced in Refs. 1, 2. 相似文献