一类奇异跳跃系统的鲁棒稳定性研究

研究了一类奇异跳跃系统的鲁棒稳定和镇定问题.在所研究的系统中,假设系数和转移率的不确定项范数有界.通过构造Lyapunov-Krasovskii函数,得到的充分条件可以保证系统在一定程度不确定性的影响下,是正则,无脉冲和均值意义下随机稳定的.最后,算例说明了所给方法的有效性.     

带Poisson 跳跃的正倒向随机延迟系统递归最优控制问题的最大值原理

本文研究了带Poisson 跳跃的正倒向随机延迟系统的递归最优控制问题. 利用经典的针状变分方法、对偶技术和带Poisson 跳跃的超前倒向随机微分方程的相关结果, 证明了最优控制的最大值原理, 包括了最优控制满足的必要条件和充分条件.     

Banach空间中含有无穷多个跳跃点的一阶脉冲积分-微分方程的无穷边值问题

通过建立一个新的比较引理,应用上下解方法和单调迭代技术,研究了Banach空间中含有无穷多个跳跃点的一阶脉冲积分-微分方程无穷边值问题在任意闭区间上最小解和最大解的存在性.     

随机脉冲随机微分方程解的存在唯一性

提出了随机脉冲随机微分方程模型,其中所谓的随机脉冲是指脉冲幅度由随机变量序列驱动,并且脉冲发生的时间也是一个随机变量序列.因此,随机脉冲随机微分方程是对带跳的随机微分方程模型的推广.利用Gronwall不等式、Lipschtiz条件和随机分析技巧,得到了随机脉冲随机微分方程的解的存在唯一性条件.     

随机脉冲微分方程的渐近p稳定性

本文研究的是随机脉冲微分方程的渐近p稳定性.首先给出一些预备知识,然后运用Lyapunov函数建立随机脉冲微分方程平凡解的渐近p稳定性的充分条件.     

跳跃扩散型离散算术平均亚式期权的近似价格公式

在标的资产价格遵循跳跃扩散过程条件下 ,研究没有封闭形式解的离散算术平均亚式期权 ,运用二阶 Edgeworth逼近得到离散算术平均亚式期权的近似价格公式 .     

白噪声作用下统一混沌系统的脉冲同步

考察了白噪声和脉冲信号联合作用下统一混沌系统的随机渐近稳定性问题,得到该随机脉冲系统的比较系统,从而由该确定性比较系统的稳定性得到原随机脉冲系统的随机渐近稳定性.并从理论上得到能使该随机脉冲系统随机渐近稳定的参数取值范围,最后用数值仿真验证了理论结果的正确性.     

Markov调制的无穷时滞脉冲随机泛函微分方程一般衰减意义下p阶矩和几乎必然稳定性（英文）

本文研究Markov调制的无穷时滞脉冲随机泛函微分方程一般衰减意义下p阶矩和几乎必然稳定性.我们运用Lyapunov函数,Razumikhin技巧和随机分析的方法,首先研究一般衰减意义下p阶矩稳定性.然后,运用Borel-Cantelli引理讨论一般衰减意义下几乎必然稳定性.推广并改进了已有文献的一些结果.最后,给出一个实例解释所得结果.     

基于模块脉冲函数的非线性随机It\^{o}-Volterra积分方程数值解[英文]

为求解非线性随机It\^{o}-Volterra积分方程, 本文介绍了一种基于模块脉冲函数的有效数值方法. 运用模块脉冲函数的积分算子矩阵将非线性随机积分方程转化为代数方程. 通过误差分析, 证明该方法收敛速度良好. 最后, 利用实例验证了此方法的有效性.     

Vasicek债券定价模型的推广形式

V asicek债券定价模型假定即期利率r(t)遵循O-U过程,利率的长期均值θ为一个常数.对此进行推广,假设θ遵循一个离散跳跃过程,跳跃的次数与幅度由中央银行根据物价指数确定,建立一个新的模型.运用Ito引理和无套利原理给出到期日价值为1的零息票债券的定价公式.     

Novel robust stability criteria for uncertain stochastic Hopfield neural networks with time-varying delays

The problem of stochastic robust stability of a class of stochastic Hopfield neural networks with time-varying delays and parameter uncertainties is investigated in this paper. The parameter uncertainties are time-varying and norm-bounded. The time-delay factors are unknown and time-varying with known bounds. Based on Lyapunov–Krasovskii functional and stochastic analysis approaches, some new stability criteria are presented in terms of linear matrix inequalities (LMIs) to guarantee the delayed neural network to be robustly stochastically asymptotically stable in the mean square for all admissible uncertainties. Numerical examples are given to illustrate the effectiveness and less conservativeness of the developed techniques.     

带马尔可夫跳的随机Hopfield神经网络的以分布渐近稳定性

讨论了带马尔可夫跳的随机Hopfield神经网络的以分布渐近稳定性.通过构造合适的Lyapunov函数,获得了判定带马尔可夫跳的随机Hopfield神经网络的以分布渐近稳定性的充分条件.     

STABILITY ANALYSIS OF THE SPLIT-STEP THETA METHOD FOR NONLINEAR REGIME-SWITCHING JUMP SYSTEMS

In this paper,we investigate the stability of the split-step theta(SST)method for a class of nonlinear regime-switching jump systems–neutral stochastic delay differential equations(NSDDEs)with Markov switching and jumps.As we know,there are few results on the stability of numerical solutions for NSDDEs with Markov switching and jumps.The purpose of this paper is to enrich conclusions in such respect.It first devotes to show that the trivial solution of the NSDDE with Markov switching and jumps is exponentially mean square stable and asymptotically mean square stable under some suitable conditions.If the drift coefficient also satisfies the linear growth condition,it then proves that the SST method applied to the NSDDE with Markov switching and jumps shares the same conclusions with the exact solution.Moreover,a numerical example is demonstrated to illustrate the obtained results.     

Exponential Stability of Discrete-Time Delayed Hopfield Neural Networks with Stochastic Perturbations and Impulses

This paper derives some sufficient conditions for exponential stability in the mean square of stochastic discrete-time delayed Hopfield neural networks (DHNN) with impulse effects. The Lyapunov–Krasovskii stability theory, Halanay inequality, and linear matrix inequality (LMI) are employed to investigate the problem. It is shown that the impulses in certain regions might preserve the stability property of the DHNN when the impulses-free part converges to its equilibrium point. Moreover, the feasible interval of the jump operator is also derived.     

The split-step θ-methods for stochastic delay Hopfield neural networks

In this paper, we construct a new split-step numerical method for stochastic delay Hopfield neural networks. The main aim of this paper is to investigate the mean-square stability of this split-step θ-methods for stochastic delay Hopfield neural networks. It is proved that the split-step θ-methods are mean-square stable under suitable conditions. Numerical experiments verify the numerical stability results obtained from theory. A comparison between this work and Ronghua et al. [8] is also discussed in the example.     

随机Hopfield时滞神经网络均方指数稳定性: LMI方法

该文通过系统变换技巧, 构造出新型的Lyapunov泛函. 利用此Lyapunov泛函, 基于线性矩阵不等式, 得到了随机Hopfield时滞神经网络与时滞相关及与时滞无关均方指数稳定性新的充分条件. 数值例子表明, 与已有结果相比, 该文的结果具有较少的保守性.     

随机泛函微分方程的渐近稳定性

应用多个Liapunov函数讨论了随机泛函微分方程解的渐近行为,建立了确定这种方程解的极限位置的充分条件,并且从这些条件得到了随机泛函微分方程渐近稳定性的有效判据,使实际应用中构造Liapunov函数更为方便．同时也说明了该结果包含了经典的随机泛函微分方程稳定性结果为其特殊情况．最后给出的结果在随机Hopfield神经网络中的应用．     

Mean‐square stability of the backward Euler–Maruyama method for neutral stochastic delay differential equations with jumps

This paper is mainly considered whether the mean‐square stability of neutral stochastic delay differential equations (NSDDEs) with jumps is shared with that of the backward Euler–Maruyama method. Under the one‐sided Lipschitz condition and the linear growth condition, the trivial solution of NSDDEs with jumps is proved to be mean‐square stable by using the functional comparison principle and the Barbalat's lemma. It is shown that the backward Euler–Maruyama method can reproduce the mean‐square stability of the trivial solution under the same conditions. The implicit backward Euler–Maruyama method shows better characteristic than the explicit Euler–Maruyama method for the reason that it works without the linear growth condition on the drift coefficient. Compared with some existing results, our results do not need to add extra condition on the neutral part. The conclusions can be applied to NSDDEs and SDDEs with jumps. The effectiveness of the theoretical results is illustrated by an example. Copyright © 2016 John Wiley & Sons, Ltd.     

Delay-dependent robust exponential state estimation of Markovian jumping fuzzy Hopfield neural networks with mixed random time-varying delays

This paper investigates delay-dependent robust exponential state estimation of Markovian jumping fuzzy neural networks with mixed random time-varying delay. In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the robust exponential state estimation of Markovian jumping Hopfield neural networks with mixed random time-varying delays. Moreover probabilistic delay satisfies a certain probability-distribution. By introducing a stochastic variable with a Bernoulli distribution, the neural networks with random time delays is transformed into one with deterministic delays and stochastic parameters. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delays, the dynamics of the estimation error is globally exponentially stable in the mean square. Based on the Lyapunov–Krasovskii functional and stochastic analysis approach, several delay-dependent robust state estimators for such T–S fuzzy Markovian jumping Hopfield neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving a delay-dependent LMI. Finally some numerical examples are provided to demonstrate the effectiveness of the proposed method.     

一类随机BAM细胞神经网络的指数稳定性

研究了一类随机BAM细胞神经网络的指数稳定性,利用Lyapunov函数理论、It公式和线性矩阵不等式方法,建立了这种细胞神经网络均方指数稳定性判定的充分性条件.