MEAN CONVERGENCE OF SOME POSITIVE OPERATORS

L'convergence with 0<p<infinity of the positive operators F.(da,f) and G.(da,f), introduced by Nevai P. [3], and Qn,8(da,f), introduced by Hermann T. and Vertesi P. [1] to f is an element of C[-1,1] is proved to hold for very general measures.

MEAN CONVERGENCE OF HERMITE-FEJER TYPE INTERPOLATION ON AN ARBITRARY SYSTEM OF NODES

In this paper sufficient conditions for mean convergence and rate of convergence of Hermite-Fejer type interpolation in the Lp norm on an arbitrary system of nodes are presented.

The Mean Convergence Order of Extended Hermite-Fejér Interpolation Operators

Weighted Lp mean convergence of Extended Hermite-Fejer operators based on the zeros of orthogonal polynomials with respct to the general weight and Jacobi weight is investigated. Suf ficient conditions for such convergence for all continuous functions are given.

MEAN CONVERGENCE OF HERMITE-FEJER TYPE INTERPOLATION

L' convergence of Hermite-Fejer interpolation and quasi-Hermite-Fejer interpolation based upon ze- ros of general orthogonal polynomials is investigated. This paper 'almost' characterizes such convergence for all continuous functions.

WEIGHTED MEAN CONVERGENCE OF HAKOPIAN INTERPOLATION ON THE DISK

In this paper, we study weighted mean integral convergence of Hakopian interpolation on the unit disk D. We show that the inner product between Hakopian interpolation polynomial Hn（f;x,y） and a smooth function g（x,y） on D converges to that of f（x,y） and g（x,y） on D when n →∞ , provided f（x,y） belongs to C（D） and all first partial derivatives of g（x,y） belong to the space LipM^α（0 〈 α ≤1）. We further show that provided all second partial derivatives of g（x,y） also belong to the space LipM^α and f（x,y） belongs to C^1 （D）, the inner product between the partial derivative of Hakopian interpolation polynomial δ/δx Hn（f;z,y） and g（x,y） on D converges to that between δ/δxf（x,y） and g（x,y） on D when n →∞. oo.

Some Mean Convergence Theorems for the Maximum of Normed Double Sums of Banach Space Valued Random Elements

In this correspondence,we establish mean convergence theorems for the maximum of normed double sums of Banach space valued random elements.Most of the results pertain to random elements which are M-dependent.We expand and improve a number of particular cases in the literature on mean convergence of random elements in Banach spaces.One of the main contributions of the paper is to simplify and improve a recent result of Li,Presnell,and Rosalsky[Journal of Mathematical Inequalities,16,117–126(2022)].A new maximal inequality for double sums of M-dependent random elements is proved which may be of independent interest.The sharpness of the results is illustrated by four examples.

Conditional mean convergence theorems of conditionally dependent random variables under conditions of integrability

We give the conditionally residual h-integrability with exponent r for an array of random variables and establish the conditional mean convergence of conditionally negatively quadrant dependent and conditionally negative associated random variables under this integrability. These results generalize and improve the known ones.

CONVERGENCE OF A CLASS OF MEANS OF H^p FUNCTIONS(0

In this paper we study the convergence nf a class of means on H^p(G)(0<p<1),the means take the Bochner-Riesz means in[1],the generalized Bochner-Riesz means in[2],and the operators T^(Φ_r)in[3]as special cases.We obtain weak-type estimates for the associated maximal operators and the maximal mean boundedness for the means.

ON THE LOCALIZATION AND CONVERGENCE OF MULTIPLE FOURIER INTEGRAL BY BOCHNER-RIESZ MEANS

In this paper we consider lim _(R-) B_R^(f,x_0), in one case that f_x_0 (t) is a ABMV function on [0, ∞], and in another case that f∈L_(m-1)~1(R~) and x^k/~kf∈BV(R) when |k| = m-1 and f(x) = 0 when |x -x_0|<δ for some δ>0. Our theormes improve the results of Pan Wenjie ([1]).

Finite Difference Scheme for Solving Parabolic Partial Differential Equations with Random Variable Input under Mean Square Sense

This study deal with seven points finite difference method to find the approximation solutions in the area of mean square calculus solutions for linear random parabolic partial differential equations. Several numerical examples are presented to show the ability and efficiency of this method.

Theoretical convergence analysis of complex Gaussian kernel LMS algorithm

With the vigorous expansion of nonlinear adaptive filtering with real-valued kernel functions,its counterpart complex kernel adaptive filtering algorithms were also sequentially proposed to solve the complex-valued nonlinear problems arising in almost all real-world applications.This paper firstly presents two schemes of the complex Gaussian kernel-based adaptive filtering algorithms to illustrate their respective characteristics.Then the theoretical convergence behavior of the complex Gaussian kernel least mean square（LMS） algorithm is studied by using the fixed dictionary strategy.The simulation results demonstrate that the theoretical curves predicted by the derived analytical models consistently coincide with the Monte Carlo simulation results in both transient and steady-state stages for two introduced complex Gaussian kernel LMS algonthms using non-circular complex data.The analytical models are able to be regard as a theoretical tool evaluating ability and allow to compare with mean square error（MSE） performance among of complex kernel LMS（KLMS） methods according to the specified kernel bandwidth and the length of dictionary.

Randomized Algorithms for Probabilistic Optimal Robust Performance Controller Design

Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.

Convergence in Distribution for Uncertain Random Sequences with Dependent Random Variables

Random variables and uncertain variables are respectively used to model randomness and uncertainty. While randomness and uncertainty always coexist in a same complex system. As an evolution of random variables and uncertain variables, uncertain random variable is introduced as a tool to deal with complex phenomena including randomness and uncertainty simultaneously. For uncertain random variables, a basic and important topic is to discuss the convergence of its sequence.Specifically, this paper focuses on studying the convergence in distribution for a sequence of uncertain random sequences with different chance distributions where random variables are not independent.And the result of this paper is a generalization of the existing literature. Relations among convergence theorems are studied. Furthermore, the theorems are explained by several examples.

Reaching a stochastic consensus in the noisy networks of linear MIMO agents:Dynamic output-feedback and convergence rate

This paper addresses the leader-following consensus problem of linear multi-agent systems(MASs) with communication noise. Each agent's dynamical behavior is described by a linear multi-input and multi-output(MIMO) system, and the agent's full state is assumed to be unavailable. To deal with this challenge, a state observer is constructed to estimate the agent's full state. A dynamic output-feedback based protocol that is based on the estimated state is proposed. To mitigate the effect of communication noise, noise-attenuation gains are also introduced into the proposed protocol. In this study, each agent is allowed to have its own noise-attenuation gain. It is shown that the proposed protocol can solve the mean square leader-following consensus problem of a linear MIMO MAS. Moreover, if all noise-attenuation gains are of Q(t-β), where b∈(0,1), the convergence rate of the MAS can be quantitatively analyzed. It turns out that all followers' states converge to the leader's state in the mean square sense at a rate of O(t-β).

Random Double Tensors Integrals

In this work,we try to build a theory for random double tensor integrals(DTI).We begin with the definition of DTI and discuss how randomness structure is built upon DTI.Then,the tail bound of the unitarily invariant norm for the random DTI is established and this bound can help us to derive tail bounds of the unitarily invariant norm for various types of two tensors means,e.g.,arithmetic mean,geometric mean,harmonic mean,and general mean.By associating DTI with perturbation formula,i.e.,a formula to relate the tensor-valued function difference with respect the difference of the function input tensors,the tail bounds of the unitarily invariant norm for the Lipschitz estimate of tensor-valued function with random tensors as arguments are derived for vanilla case and quasi-commutator case,respectively.We also establish the continuity property for random DTI in the sense of convergence in the random tensor mean,and we apply this continuity property to obtain the tail bound of the unitarily invariant norm for the derivative of the tensor-valued function.

WONG-ZAKAI APPROXIMATIONS FOR STOCHASTIC VOLTERRA EQUATIONS

In this paper,we shall prove a Wong-Zakai approximation for stochastic Volterra equations under appropriate assumptions.We may apply it to a class of stochastic differential equations with the kernel of fractional Brownian motion with Hurst parameter H∈(1/2,1)and subfractional Brownian motion with Hurst parameter H∈(1/2,1).As far as we know,this is the first result on stochastic Volterra equations in this topic.