Random Crank-Nicolson Scheme for Random Heat Equation in Mean Square Sense

The goal of computational science is to develop models that predict phenomena observed in nature. However, these models are often based on parameters that are uncertain. In recent decades, main numerical methods for solving SPDEs have been used such as, finite difference and finite element schemes [1]-[5]. Also, some practical techniques like the method of lines for boundary value problems have been applied to the linear stochastic partial differential equations, and the outcomes of these approaches have been experimented numerically [7]. In [8]-[10], the author discussed mean square convergent finite difference method for solving some random partial differential equations. Random numerical techniques for both ordinary and partial random differential equations are treated in [4] [10]. As regards applications using explicit analytic solutions or numerical methods, a few results may be found in [5] [6] [11]. This article focuses on solving random heat equation by using Crank-Nicol- son technique under mean square sense and it is organized as follows. In Section 2, the mean square calculus preliminaries that will be required throughout the paper are presented. In Section 3, the Crank-Nicolson scheme for solving the random heat equation is presented. In Section 4, some case studies are showed. Short conclusions are cleared in the end section.

Mean square stability of recurrent neural networks with random delay and Markovian switching

To establish easily proved conditions under which the random delayed recurrent neural network with Markovian switching is mean-square stability,the evolution of the delay was modeled by a continuous-time homogeneous Markov process with a finite number of states.By employing Lyapunov-Krasovskii functionals and conducting stochastic analysis,a linear matrix inequality (LMI) approach was developed to derive the criteria for mean-square stability,which can be readily checked by some standard numerical packages such as the Matlab LMI Toolbox.A numerical example was exploited to show the usefulness of the derived LMI-based stability conditions.

Mean-Square Exponential Input-to-State Stability of Numerical Solutions for Stochastic Control Systems

这份报纸处理随机的控制系统(SCS ) 的数字解决方案的吝啬平方的指数的 input-to-state 稳定性(expbe ) 。第一，一个有限时间的强壮的集中条件成立为，这被显示出随机 ? SCS 上的方法。那么，如果并且仅当，我们能看到 SCS 的吝啬平方的 expbe 成立的随机 ? 方法(为足够地小的步尺寸) 在有限时间的强壮的集中条件下面被保存。为有片面 Lipschitz 飘移的 SCS 的一个班，第二，它被证明那二含蓄的 Euler 方法(为任何步尺寸) 能继承 SCS 的吝啬平方的 expbe 性质。最后，数字例子证实在这研究介绍的定理的正确性。

Mean Square Heun’s Method Convergent for Solving Random Differential Initial Value Problems of First Order

This paper deals with the construction of Heun’s method of random initial value problems. Sufficient conditions for their mean square convergence are established. Main statistical properties of the approximations processes are computed in several illustrative examples.

Mean Square Convergent Finite Difference Scheme for Stochastic Parabolic PDEs

Stochastic partial differential equations (SPDEs) describe the dynamics of stochastic processes depending on space-time continuum. These equations have been widely used to model many applications in engineering and mathematical sciences. In this paper we use three finite difference schemes in order to approximate the solution of stochastic parabolic partial differential equations. The conditions of the mean square convergence of the numerical solution are studied. Some case studies are discussed.

Mean Square Numerical Methods for Initial Value Random Differential Equations

In this paper, the random Euler and random Runge-Kutta of the second order methods are used in solving random differential initial value problems of first order. The conditions of the mean square convergence of the numerical solutions are studied. The statistical properties of the numerical solutions are computed through numerical case studies.

Asymptotical mean square stability of cellular neural networks with random delay

In this paper,the asymptotical mean-square stability analysis problem is considered for a class of cellular neural networks (CNNs) with random delay. Compared with the previous work,the delay is modeled by a continuous-time homogeneous Markov process with a finite number of states. The main purpose of this paper is to establish easily verifiable conditions under which the random delayed cellular neural network is asymptotic mean-square stability. By using some stochastic analysis techniques and Lyapunov-Krasovskii functional,some conditions are derived to ensure that the cellular neural networks with random delay is asymptotical mean-square stability. A numerical example is exploited to show the vadlidness of the established results.

Stability and output feedback stabilization for systems with Markovian switching and impulse effects

We investigate the exponential stability in the mean square sense for the systems with Markovian switching and impulse effects.Based on the statistic property of the Markov process,a stability criterion is established.Then,by the parameterizations via a family of auxiliary matrices,the dynamical output feedback controller can be solved via an LMI approach,which makes the closed-loop system exponentially stable.A numerical example is given to demonstrate the method.

Delay-dependent exponential stability criteria for stochastic systems with polytopic-type uncertainties

This paper considers the problem of delay-dependent exponential stability in mean square for stochastic systems with polytopic-type uncertainties and time-varying delay. Applying the descriptor model transformation and introducing free weighting matrices, a new type of Lyapunov-Krasovskii functional is constructed based on linear matrix inequalities （LMIs）, and some new delay-dependent criteria are obtained. These criteria include the delay-independent/rate- dependent and delay-dependent/rate-independent exponential stability criteria. These new criteria are less conservative than existing ones. Numerical examples demonstrate that these new criteria are effective and are an improvement over existing ones.

NONLINEAR RANDOM STABILITY OF VISCOELASTIC CABLE WITH SMALL CURVATURE

The nonlinear planar mean square response and the random stability of a viscoelastic cable that has a small curvature and subjects to planar narrow band random excitation is studied. The Kelvin viscoelastic constitutive model is chosen to describe the viscoelastic property of the cable material. A mathematical model that describes the nonlinear planar response of a viscoelastic cable with small equilibrium curvature is presented first. And then a method of investigating the mean square response and the almost sure asymptotic stability of the response solution is presented and regions of instability are charted. Finally , the almost sure asymptotic stability condition of a viscoelastic cable with small curvature under narrow band excitation is obtained.

ON EXPONENTIAL STABILITY OF NON-AUTONOMOUS STOCHASTIC SEMILINEAR EVOLUTION EQUATIONS

Sufficient conditions for the exponential stability of a class of nonlinear, non-autonomous stochastic differential equations in infinite dimensions are studied. The analysis consists of introducing a suitable approximating solution systems and usig a limiting argument to pass on stability of strong solutions to mild ones. Consequently, under these conditions the random attractors of given stochastic systems are reduced to zero with exponential decay. Lastly, two examples are investigated to illustrate the theory.

Effect of Exercise with Rhythmic Auditory Stimulation on Muscle Coordination and Gait Stability in Patients with Diabetic Peripheral Neuropathy: A Randomized Controlled Trial

Background: Diabetic peripheral neuropathy (DPN) changes leg muscle coordination during walking and reduces stability. The aim of this study was to determine whether rhythmic auditory stimulation (RAS) affected the gait performance of patients with DPN. Methods: Forty DPN patients (mean age, 59.1 ± 9.4 y) were randomly allocated to RAS and control groups in equal numbers. The participants in each group underwent 2 weeks of supervised rehabilitative treatment (40 min/day) as inpatients. This included walking twice a day, during which the RAS group participants walked in time with a metronome set at a self-chosen, comfortable rate. We compared gait function, lower limb muscle co-contraction, and gait stability before and after the intervention for both groups, calculated the change in score for each parameter, and assessed differences between the groups with unpaired t-tests and ANCOVA. Results: RAS was associated with significant improvement in all parameters. In the control group, there was no improvement in cadence, co-contraction, or gait stability (vertical). Compared with the control group, the RAS group showed improvement in co-contraction and gait stability. Conclusion: RAS may be helpful for improving the lower limb muscle coordination and gait function of DPN patients.

Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

In this paper, the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks （CRNNs） with stochastic delay. Different from the common assumptions on time delays, it is assumed that the probability distribution of the delay taking values in some intervals is known a priori. By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique （the reciprocally convex combination method）, less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities （LMIs）. Two numerical examples show that our results are better than the existing ones.

A DELAY-DEPENDENT STABILITY CRITERION FOR NONLINEAR STOCHASTIC DELAY-INTEGRO-DIFFERENTIAL EQUATIONS

A type of complex systems under both random influence and memory effects is considered. The systems are modeled by a class of nonlinear stochastic delay-integrodifferential equations. A delay-dependent stability criterion for such equations is derived under the condition that the time lags are small enough. Numerical simulations are presented to illustrate the theoretical result.

随机微分方程Milstein方法的稳定性

基于随机微分方程的两类试验方程 ,即噪声为增加噪声和附加噪声的两种情况 ,讨论了求解标量自治随机微分方程的Milstein数值方法的三种稳定性 :A 稳定性、均方稳定性和T 稳定性 .得出确定性情形的A 稳定性延伸到随机性情形时保持不变的结论 。

欠驱动船舶航迹Backstepping自适应模糊控制

针对欠驱动船舶直线航迹跟踪问题,提出一种Backstepping自适应模糊控制方法.在模糊逼近误差存在未知上确界的假设条件下,基于Lyapunov理论证明了闭环系统在所有信号一致最终有界意义下具有均方意义稳定性本文提出的控制器具有设计直观和结构简洁的特点,并且对参数摄动和外界干扰都具有良好的鲁棒性.在状态变量和控制输入共同约束下的仿真实验验证了该方法的有效性.

基于BaGa_(4)Se_(7)晶体的中红外辐射源稳定性实验研究

在实际应用中,光源的输出能量稳定性影响着光源在各种应用中的可靠性和性能,如激光治疗、光谱分析、光电对抗以及光成像等领域。本文首先对光学参量振荡器(OPO)系统从激光泵浦源、谐振腔结构和非线性晶体三个方面对输出能量稳定性进行分析。实验方面,使用BaGa_(4)Se_(7)(BGSe)晶体搭建脉冲激光泵浦的3~8μm中红外OPO辐射源,从激光泵浦源和OPO系统两部分对输出能量波动性进行了详细的测量和分析。当输入泵浦能量为30 mJ,正入射输出4.5μm波长时,平均能量为2.151 mJ,标准差为56.49μJ,计算RMS波动为2.62%。根据分析得出,随着激光增益的提高和腔损耗的减小,输出脉冲的波动减小。该研究为不同应用场景下BGSe晶体的红外辐射源的工作参数的选择提供了有益的指导。

随机延迟微分方程的Milstein方法的非线性均方稳定性

本文针对一般的非线性随机延迟微分方程,证明了当系统理论解满足均方稳定性条件时,则当方程的漂移和扩散项满足一定的条件时,Milstein方法也是均方稳定的.数学实验进一步验证了我们的结论.