<Emphasis Type="Italic">p</Emphasis>th moment exponential synchronization for stochastic delayed Cohen–Grossberg neural networks with Markovian switching

This paper is a contribution to the analysis of the pth moment exponential synchronization problem for a class of stochastic delayed Cohen–Grossberg neural networks with Markovian switching. The jumping parameters are determined by a continuous-time, discrete-state Markov chain, and the delays are time-varying delays.     

State estimation for Markovian jumping recurrent neural networks with interval time-varying delays

The paper is concerned with the state estimation problem for a class of neural networks with Markovian jumping parameters. The neural networks have a finite number of modes and the modes may jump from one to another according to a Markov chain. 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 are globally stable in the mean square. A new type of Markovian jumping matrix P _i is introduced in this paper. The discrete delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities (LMIs). Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI conditions.     

Extremal exponents of random dynamical systems do not vanish

A family of random diffeomorphisms on a manifoldM is said to be a random dynamical system or RDS if it has the so-called cocycle property. The multiplicative ergodic theorem assignsd (=dimM) Lyapunov exponents to every invariant measure of the system. Take the maximum of the leading exponents associated with the various invariant measures. The resulting number is said to be the maximal exponent of the system. The minimal exponent is defined in a similar fashion. It is shown that the minimal exponent of an RDS on a compact manifold is negative, provided not all invariant measures are determined by the future of. A similar statement relates the maximal exponent with the past of. We proceed by introducing Markov systems and Markov measures. This notion covers flows of stochastic differential equations as well as products of random diffeomorphisms in Markovian dependence, in particular, products of iid diffeomorphisms. Markov measures are characterized by the fact that they are functionals of the past. Consequently, if there exists a non-Markovian invariant measure, then the maximal exponent does not vanish. Typically, Markov systems do have non-Markovian invariant measures. Finally, for linear systems we recover results of Ledrappier. In particular, these results provide another proof of Furstenberg's theorem on the positivity of the leading exponent of a product of iid unimodular matrices.     

Dynamics on certain sets of stochastic matrices

We study iteration of polynomials on symmetric stochastic matrices. In particular, we focus on a certain one-parameter family of quadratic maps which exhibits chaotic behavior for a wide range of the parameters. The well-known dynamical behavior of the quadratic family on the interval, and its dependence on the parameter, is reproduced on the spectrum of the stochastic matrices. For certain subclasses of stochastic matrices the referred dynamical behavior is also obtained in the matrix entries. Since a stochastic matrix characterizes a Markov chain, we obtain a discrete dynamical system on the space of reversible Markov chains. Therefore, depending on the parameter, there are initial conditions for which the corresponding reversible Markov chains will lead under iteration to a fixed point, to a periodic point, or to an aperiodic point. Moreover, there are sensitivity to initial conditions and the coexistence of infinite repulsive periodic orbits, both features of chaos.     

Markovian risk process

A Markovian risk process is considered in this paper,which is the gener- alization of the classical risk model.It is proper that a risk process with large claims is modelled as the Markovian risk model.In such a model,the occurrence of claims is described by a point process {N(t)}_(t≥0) with N(t) being the number of jumps during the interval(0,t]for a Markov jump process.The ruin probabilityΨ(u)of a company facing such a risk model is mainly studied.An integral equation satisfied by the ruin probability functionΨ(u)is obtained and the bounds for the convergence rate of the ruin probabilityΨ(u)are given by using a generalized renewal technique developed in the paper.     

Robust observer for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

The robust observer problem is considered in this paper for a class of discrete-time neural networks with Markovian jumping parameters and mode-dependent time delays which are in both discrete-time form and finite distributed form. The neural network switches from one mode to another controlled by a Markov chain with known transition probability. Time-delays considered in this paper are mode-dependent which may reflect a more realistic version of the neural network. By using the Lyapunov functional method and the techniques of linear matrix inequalities (LMIs), sufficient conditions are established in terms of LMIs that ensure the existence of the robust observer. The obtained conditions are easy to be verified via the LMI toolbox. An example is presented to show the effectiveness of the obtained results.     

Mean wave propagation in a slab of one-dimensional discrete random medium

A study has been made of the propagation of time harmonic waves through a one-dimensional medium of discrete scatterers randomly positioned over a finite interval L. The random medium is modeled by a Poisson impulse process with density λ. The invariant imbedding procedure is employed to obtain a set of initial value stochastic differential equations for the field inside the medium and the reflection coefficient of the layer. By using the Markov properties of the Poisson impulse process. exact integro-differential equations of the Kolmogorov-Feller type are derived for the probability density function of the reflection coefficient and the field. When the concentration of the scatterers is low, a two variable perturbation method in small λ is used to obtain an approximate solution for the mean field. It is shown that this solution, which varies exponentially with respect to λL, agrees exactly with the mean field obtained by Feldy's approximate method.     

Complete Integrability of Shock Clustering and Burgers Turbulence

We consider scalar conservation laws with convex flux and random initial data. The Hopf–Lax formula induces a deterministic evolution of the law of the initial data. In a recent article, we derived a kinetic theory and Lax equations to describe the evolution of the law under the assumption that the initial datum is a spectrally negative Markov process. Here we show that: (i) the Lax equations are Hamiltonian and describe a principle of least action on the Markov group; (ii) the Lax equations are completely integrable and linearized via a loop-group factorization of operators; (iii) the associated zero-curvature equations can be solved via inverse scattering. Our results are rigorous for N-dimensional approximations of the Lax equations, and yield formulas for the limit N → ∞. The main observation is that the Lax equations and zero-curvature equations are a Markovian analog of known integrable systems (geodesic flow on Lie groups and the N-wave model respectively). This allows us to introduce a variety of methods from the theory of integrable systems.     

Exponential stability in the mean square for stochastic neural networks with mixed time-delays and Markovian jumping parameters

In this paper, the stability analysis problem is considered for a class of stochastic neural networks with mixed time-delays and Markovian jumping parameters. The mixed delays include discrete and distributed time-delays, and the jumping parameters are generated from a continuous-time discrete-state homogeneous Markov process. The aim of this paper is to establish some criteria under which the delayed stochastic neural networks are exponentially stable in the mean square. By constructing suitable Lyapunov functionals, several stability conditions are derived on the basis of inequality techniques and the stochastic analysis. An example is also provided in the end of this paper to demonstrate the usefulness of the proposed criteria.     

Dynamic Systems with Poisson White Noise

Methods are developed for finding properties of the output of linear and nonlinear dynamic systems to random actions represented by Poisson white noise and filtered Poisson processes. The Poisson white noise can be viewed as a sequence of independent, identically distributed pulses arriving at random times. The filtered Poisson process is the output of a linear filter to Poisson white noise. Three methods are considered for finding output properties. If the input has infrequent or frequent pulses, output properties can be obtained from a Markov model or the assumption that the input is a Gaussian white noise, respectively. Otherwise, a method based on Itô's formula for semimartingales is used to find output properties. Examples are used to illustrate the proposed methods.     

A Markov Chain Model for Effective Relative Permeabilities and Capillary Pressure

We present a new method for calculating the effective two-phase parameters of one-dimensional randomly heterogeneous porous media, which avoids the timeconsuming use of simulations on explicit realizations. The procedure is based on the steady state saturation distribution. The idea is to model the local variation of saturation and saturation dependent parameters as Markov chains, in such a way that the effective parameters are given by the asymptotic expectations of the chains. We derive the exact asymptotic moment equations and solve them numerically, based on their second order approximation. The method determines the effective parameters to a high degree of accuracy, even with large variations in rock properties. In particular, the capillary limit and viscous limit effective parameters are recovered exactly. The applicability of the effective parameters in the unsteady state case is studied by comparing the displacement production profiles in heterogeneous media and their homogenized counterpart.     

The Monte Carlo dynamics of polymer chains in sandwich brushes

We designed and developed a simple model of polymer chains. The chains consist of identical united atoms (segments) and were restricted to a simple cubic lattice with the excluded volume interactions only (an athermal system).The polymers were confined between two parallel impenetrable walls with one end of each chain was grafted to the wall. A motion of a probe single linear chain in such environment was studied. The properties of the model studied were determined from the Monte Carlo simulations employing a Metropolis-like sampling algorithm with local changes of chains’ conformation. The influence of the system density and the length of chains on the polymer mobility was studied and discussed. We found that the number of chains forming the brush was the major quantity which governed the dynamics of a probe chain, while the length of the chains in the brush also influenced the diffusivity of the probe chain. The diffusion coefficients scaled with the length of a probe chain is stronger than in the Rouse model with the exponent γ?=??1.3.     

State estimation of neural networks with time-varying delays and Markovian jumping parameter based on passivity theory

In this paper, the state estimation problem is investigated for neural networks with time-varying delays and Markovian jumping parameter based on passivity theory. The neural networks have a finite number of modes and the modes may jump from one to another according to a Markov chain. 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 stable in the mean square and passive from the control input to the output error. Based on the new Lyapunov?CKrasovskii functional and passivity theory, delay-dependent conditions are obtained in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate effectiveness of the proposed method and results.     

Global exponential estimates for uncertain Markovian jump neural networks with reaction-diffusion terms

In this paper, the robust global exponential estimating problem is investigated for Markovian jumping reaction-diffusion delayed neural networks with polytopic uncertainties under Dirichlet boundary conditions. The information on transition rates of the Markov process is assumed to be partially known. By introducing a new inequality, some diffusion-dependent exponential stability criteria are derived in terms of relaxed linear matrix inequalities. Those criteria depend on decay rate, which may be freely selected in a range according to practical situations, rather than required to satisfy a transcendental equation. Estimates of the decay rate and the decay coefficient are presented by solving these established linear matrix inequalities. Numerical examples are provided to demonstrate the advantage and effectiveness of the proposed method.     

基于马尔科夫链与鞍点逼近的模糊随机可靠性分析方法

针对同时存在随机不确定性和模糊不确定性的可靠性分析问题,提出了两种高效解决方法。一种是迭代马尔科夫链鞍点逼近法,该方法的基本思想是给定隶属水平下由迭代马尔科夫链和一次鞍点逼近法求得可靠度上下限,不同的隶属水平对应不同的可靠度上下限,遍历隶属水平的取值区间[0,1]即可求得可靠度隶属函数,与传统的两相Monte Carlo数字模拟法和迭代一次二阶矩法相比,该方法具有效率高和对非正态基本随机变量不需要进行正态转换的优点;第二种方法是迭代条件概率马尔科夫链模拟法,该方法在求解给定隶属度水平下的可靠度上下限时,由条件概率公式引入一个非线性修正因子,该因子的引入大大提高了功能函数为非线性的可靠性问题的求解精度。本文算例验证了所提方法的优越性。     

Limit and shakedown analysis under hydrogen embrittlement condition

A modified shakedown theorem and its solving technique are presented to involve hydrogen embrittlement of steel into limit and shakedown analysis. Firstly, the shakedown theorem for hydrogen embrittled material is derived from a limited kinematic hardening shakedown theorem and hydrogen enhanced localized plasticity mechanism of hydrogen embrittlement. In the presented theorem, hydrogen’s effect is taken into account by the synergistic action of both strength reduction and stress redistribution. Secondly, a novel solving technique is developed based on the basis reduction method, in which the complicated constraints in the resulting nonlinear mathematical programming are released. At last, three numerical examples are carried out to verify the performance of the proposed method and to reveal hydrogen’s effect on the limit and shakedown load of structure. The numerical results are discussed and compared with those from literatures, which proves the accuracy and high efficiency of the introduced solving technique. It is concluded that the proposed theorem can predict the limit and shakedown load of hydrogen embrittled structure reasonably.     

抗震结构时变动力可靠度分析的随机过程法

研究单自由度随机时变结构的动力响应，在地震荷载作用下，分别采用Ｐｏｉｓｓｏｎ过程，Ｍａｒｋｏｖ过程和Ｗｉｅｎｅｒ过程法研究了时变动力可靠度的首次超越问题，提出了相应的时变动力可靠度计算公式     

可靠性灵敏度函数及其特征指标的条件概率模拟求解方法

可靠性分析中基本变量分布参数为区间均匀变量时,失效概率为分布参数的函数。基于条件概率马尔科夫链模拟,提出了一种可靠性灵敏度函数的求解方法,并提出了一种新的可靠性灵敏度度量指标,它为参数可靠性灵敏度函数在参数空间上的期望。文中推导了线性极限状态正态变量下全局灵敏度函数及新指标的计算式.并提出了高效的基于条件概率马尔科夫链...     

Variational methods for the steady state response of elastic–plastic solids subjected to cyclic loads

Solids (or structures) of elastic–plastic internal variable material models and subjected to cyclic loads are considered. A minimum net resistant power theorem, direct consequence of the classical maximum intrinsic dissipation theorem of plasticity theory, is envisioned which describes the material behavior by determining the plastic flow mechanism (if any) corresponding to a given stress/hardening state. A maximum principle is provided which characterizes the optimal initial stress/hardening state of a cyclically loaded structure as the one such that the plastic strain and kinematic internal variable increments produced over a cycle are kinematically admissible. A steady cycle minimum principle, integrated form of the aforementioned minimum net resistant power theorem, is provided, which characterizes the structure’s steady state response (steady cycle) and proves to be an extension to the present context of known principles of perfect plasticity. The optimality equations of this minimum principle are studied and two particular cases are considered: (i) loads not exceeding the shakedown limit (so recovering known results of shakedown theory) and (ii) specimen under uniform cyclic stress (or strain). Criteria to assess the structure’s ratchet limit loads are given. These, together with some insensitivity features of the structure’s alternating plasticity state, provide the basis to the ratchet limit load analysis problem, for which solution procedures are discussed.     

Universal viscometric functions for dilute polymer solutions

The Gaussian approximation has been shown previously to be excellent for the treatment of hydrodynamic effects in dilute polymer solutions. However, the computational time required to find the viscometric functions in simple shear flow is prohibitively long for chains with a large number of beads. Here we introduce a new approximation which retains the accuracy of the Gaussian approximation but is significantly less computationally intensive. Thus the rheological behavior of long chains may be explored. Extrapolation of results obtained numerically for long chains to the infinite chain length limit is shown to lead to predictions independent of model parameters. As a result, within the context of the approximation introduced here, universal viscometric functions for dilute polymer solutions in simple shear flow under theta conditions are obtained.