competition interfaces and second class particles the one dimensional nearest neighbor totally asymmetric simple exclusion process can be constructed in the same space as a last passage percolation model in z 2 we show that the trajectory of a second class particle in the exclusion process can be linearly mapped into the competition interface between two growing clusters in the last passage percolation model using technology built up for geodesics in percolation we show that the competition interface converges almost surely to an asymptotic random direction as a consequence we get a new proof for the strong law of large numbers for the second class particle in the rarefaction fan and describe the distribution of the asymptotic angle of the competition interface

periodicity in bilinear lattices and the coxeter formalism abstract we introduce and study in detail so called circulant coxeter periodic elements and circulant families in a bilinear lattice k as well as their dual versions called anti circulant we show that they form a natural environment for a systematic explanation of certain cyclotomic factors of the coxeter polynomial k of k and in consequence of coxeter polynomials of algebras of finite global dimension we discuss the properties of quadratic forms induced by circulant and anti circulant families moreover we interpret the results in the language of representation theory of algebras and point out applications facts concerning tubular families in auslander reiten quivers and quadratic forms of algebras abstract considerations in bilinear lattices are illustrated with a collection of non trivial examples arising from module and derived categories the results show that techniques of linear algebra and number theory provide efficient tools for explaining various representation theoretic facts

asynchronous l 2 l filtering for discrete time stochastic markov jump systems with randomly occurred sensor nonlinearities this paper is concerned with the problem of asynchronous l 2 l filtering for discrete time stochastic markov jump systems with sensor nonlinearity the sensor nonlinearity is assumed to occur randomly according to a stochastic variable satisfying the bernoulli distribution a sufficient condition is first given such that the resultant filtering error system which is a kind of nonhomogeneous markov jump system is stochastically stable with a guaranteed l 2 l performance index then the existence criterion of the desired asynchronous filter with piecewise homogeneous markov chain is proposed in terms of a set of linear matrix inequalities a numerical example is given to show the effectiveness and potential of the developed theoretical results

noise induced stabilization of the recurrent neural networks with mixed time varying delays and markovian switching parameters the stabilization of recurrent neural networks with mixed time varying delays and markovian switching parameters by noise is discussed first a new result is given for the existence of unique states of recurrent neural networks nns with mixed time varying delays and markovian switching parameters in the presence of noise without the need to satisfy the linear growth conditions required by general stochastic markovian switching systems next a delay dependent condition for stabilization of concerned recurrent nns is derived by applying the ltd formula the gronwall inequality the law of large numbers and the ergodic property of markovian chain the results show that there always exists an appropriate white noise such that any recurrent nns with mixed time varying delays and markovian switching parameters can be exponentially stabilized by noise if the delays are sufficiently small

space time stationary solutions for the burgers equation we construct space time stationary solutions of the 1d burgers equation with random forcing in the absence of periodicity or any other compactness assumptions more precisely for the forcing given by a homogeneous poissonian point field in space time we prove that there is a unique global solution with any prescribed average ve locity these global solutions serve as one point random attractors for the infinite dimensional dynamical system associated to solutions to the cauchy problem the probability distribution of the global solutions de fines a stationary distribution for the corresponding markov process we describe a broad class of initial cauchy data for which the distribution of the markov process converges to the above stationary distribution our construction of the global solutions is based on a study of the field of action minimizing curves we prove that for an arbitrary value of the average velocity with probability 1 there exists a unique field of action minimizing curves initiated at all of the poissonian points moreover action minimizing curves corresponding to different starting points merge with each other in finite time

limiting shape for directed percolation models we consider directed first passage and last passage percolation on the nonnegative lattice z d d 2 with i i d weights at the vertices under certain moment conditions on the common distribution of the weights the limits g x lim n n 1 t nx exist and are constant a s for x r d where t z is the passage time from the origin to the vertex z z d we show that this shape function g is continuous on r d in particular at the boundaries in two dimensions we give more precise asymptotics for the behavior of g near the boundaries these asymptotics depend on the common weight distribution only through its mean and variance in addition we discuss growth models which are naturally associated to the percolation processes giving a shape theorem and illustrating various possible types of behavior with output from simulations

busemann functions and equilibrium measures in last passage percolation models the interplay between two dimensional percolation growth models and one dimensional particle processes has been a fruitful source of interesting mathematical phenomena in this paper we develop a connection between the construction of busemann functions in the hammersley last passage percolation model with i i d random weights and the existence ergodicity and uniqueness of equilibrium or time invariant measures for the related multi class interacting fluid system as we shall see in the classical hammersley model where each point has weight one this approach brings a new and rather geometrical solution of the longest increasing subsequence problem as well as a central limit theorem for the busemann function

h_ infty state estimation for discrete time complex networks with randomly occurring sensor saturations and randomly varying sensor delays in this paper the state estimation problem is investigated for a class of discrete time delay nonlinear complex networks with randomly occurring phenomena from sensor measurements the randomly occurring phenomena include randomly occurring sensor saturations rosss and randomly varying sensor delays rvsds that result typically from networked environments a novel sensor model is proposed to describe the rosss and the rvsds within a unified framework via two sets of bernoulli distributed white sequences with known conditional probabilities rather than employing the commonly used lipschitz type function a more general sector like nonlinear function is used to describe the nonlinearities existing in the network the purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that for all probabilistic sensor saturations and sensor delays the dynamics of the estimation error is guaranteed to be exponentially mean square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an h norm in terms of a novel lyapunov krasovskii functional and the kronecker product sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method a simulation example is provided to show the usefulness of the proposed state estimation conditions

on the distribution of the length of the longest increasing subsequence of random permutations let sn be the group of permutations of 1 2 n if 7r e sn we say that 7 i1 7f ik is an increasing subsequence in 7r if il i2 ik and 7r ii 7r i2 7r ik let 1n r be the length of the longest increasing subsequence for example if n 5 and 7r is the permutation 5 1 3 2 4 in one line notation thus 7r 1 5 7r 2 1 then the longest increasing subsequences are 1 2 4 and 1 3 4 and n 3 equip sn with uniform distribution

limit theorems for height fluctuations in a class of discrete space and time growth models we introduce a class of one dimensional discrete space discrete time stochastic growth models described by a height function ht x with corner initialization we prove with one exception that the limiting distribution function of ht x suitably centered and normalized equals a fredholm determinant previously encountered in random matrix theory in particular in the universal regime of large x and large t the limiting distribution is the fredholm determinant with airy kernel in the exceptional case called the critical regime the limiting distribution seems not to have previously occurred the proofs use the dual rsk algorithm gessel s theorem the borodin okounkov identity and a novel rigorous saddle point analysis in the fixed x large t regime we find a brownian motion representation this model is equilvalent to the seppalainen johansson model hence some of our results are not new but the proofs are

finite time control of discrete time linear systems in this note we consider the finite time stabilization of discrete time linear systems subject to disturbances generated by an exosystem finite time stability can be used in all those applications where large values of the state should not be attained for instance in the presence of saturations the main result provided in the note is a sufficient condition for finite time stabilization via state feedback this result is then used to find some sufficient conditions for the existence of an output feedback controller guaranteeing finite time stability all the conditions are then reduced to feasibility problems involving linear matrix inequalities lmis some numerical examples are presented to illustrate the proposed methodology

almost sure exponential stability of recurrent neural networks with markovian switching this paper presents new stability results for recurrent neural networks with markovian switching first algebraic criteria for the almost sure exponential stability of recurrent neural networks with markovian switching and without time delays are derived the results show that the almost sure exponential stability of such a neural network does not require the stability of the neural network at every individual parametric configuration next both delay dependent and delay independent criteria for the almost sure exponential stability of recurrent neural networks with time varying delays and markovian switching parameters are derived by means of a generalized stochastic halanay inequality the results herein include existing ones for recurrent neural networks without markovian switching as special cases finally simulation results in three numerical examples are discussed to illustrate the theoretical results

event triggered fuzzy adaptive quantized control for nonlinear multi agent systems in nonaffine pure feedback form abstract in this paper we address the problem of event triggered fuzzy adaptive quantized control for stochastic nonlinear non affine pure feedback multi agent systems the fuzzy logic system is used to estimate the stochastic disturbance term and unknown nonlinear functions a nonlinearity decomposition method of asymmetric hysteresis quantizer is proposed by applying sector bound property moreover to reduce the communication burden an adaptive event triggered protocol with a varying threshold is constructed based on the backstepping technique and stochastic lyapunov function method a novel adaptive event triggered fuzzy control protocol and adaptive laws are constructed by using stochastic lyapunov stability theory it is demonstrated that all signals are bounded in the closed loop systems in probability and all the outputs of followers converge to the neighborhood of the leader output simulation results illustrate the effectiveness of our proposed scheme

robust estimation for neural networks with randomly occurring distributed delays and markovian jump coupling this paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays where the polytopic model is employed to describe the parameter uncertainty a set of bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays novel state estimators based on the local coupling structure are proposed to make full use of the coupling information the augmented estimation error system is obtained based on the kronecker product a new lyapunov function which depends both on the polytopic uncertainty and the coupling information is introduced to reduce the conservatism sufficient conditions which guarantee the stochastic stability and the l 2 l infty performance of the augmented estimation error system are established then the estimator gains are further obtained on the basis of these conditions finally a numerical example is used to prove the effectiveness of the results

passivity analysis for discrete time stochastic markovian jump neural networks with mixed time delays in this paper passivity analysis is conducted for discrete time stochastic neural networks with both markovian jumping parameters and mixed time delays the mixed time delays consist of both discrete and distributed delays the markov chain in the underlying neural networks is finite piecewise homogeneous by introducing a lyapunov functional that accounts for the mixed time delays a delay dependent passivity condition is derived in terms of the linear matrix inequality approach the case of markov chain with partially unknown transition probabilities is also considered all the results presented depend upon not only discrete delay but also distributed delay a numerical example is included to demonstrate the effectiveness of the proposed methods

state estimation for stochastic complex networks with switching topology this technical note studies the state estimation problem for stochastic complex networks with switching topology a set of bernoulli random variables are used to describe the switching behavior of the network by using the structure of the extended kalman filter ekf a recursive estimator is developed for each node to guarantee an optimized upper bound on the state estimation error covariance despite the switching coupling and linearization errors compared with the existing results on state estimation for stochastic complex networks a distinct feature for the proposed estimator is that the estimation errors for all nodes are not formulated in an augmented vector so that the gain matrix can be determined separately for each node by solving two riccati like difference equations based on the stochastic analysis theory it is shown that the estimation error is bounded in mean square under certain conditions simulation results are provided to verify the effectiveness of the proposed estimator

dissipativity analysis for discrete time stochastic neural networks with time varying delays in this paper the problem of dissipativity analysis is discussed for discrete time stochastic neural networks with time varying discrete and finite distributed delays the discretized jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically stable in the mean square and strictly q s r y dissipative which is delay dependent in the sense that it depends on not only the discrete delay but also the finite distributed delay based on the dissipativity criterion some special cases are also discussed compared with the existing ones the merit of the proposed results in this paper lies in their reduced conservatism and less decision variables three examples are given to illustrate the effectiveness and benefits of our theoretical results

stability analysis of distributed delay neural networks based on relaxed lyapunov krasovskii functionals this paper revisits the problem of asymptotic stability analysis for neural networks with distributed delays the distributed delays are assumed to be constant and prescribed since a positive definite quadratic functional does not necessarily require all the involved symmetric matrices to be positive definite it is important for constructing relaxed lyapunov krasovskii functionals which generally lead to less conservative stability criteria based on this fact and using two kinds of integral inequalities a new delay dependent condition is obtained which ensures that the distributed delay neural network under consideration is globally asymptotically stable this stability criterion is then improved by applying the delay partitioning technique two numerical examples are provided to demonstrate the advantage of the presented stability criteria

bounded h_ infty synchronization and state estimation for discrete time varying stochastic complex networks over a finite horizon in this paper new synchronization and state estimation problems are considered for an array of coupled discrete time varying stochastic complex networks over a finite horizon a novel concept of bounded h synchronization is proposed to handle the time varying nature of the complex networks such a concept captures the transient behavior of the time varying complex network over a finite horizon where the degree of bounded synchronization is quantified in terms of the h norm a general sector like nonlinear function is employed to describe the nonlinearities existing in the network by utilizing a timevarying real valued function and the kronecker product criteria are established that ensure the bounded h synchronization in terms of a set of recursive linear matrix inequalities rlmis where the rlmis can be computed recursively by employing available matlab toolboxes the bounded h state estimation problem is then studied for the same complex network where the purpose is to design a state estimator to estimate the network states through available output measurements such that over a finite horizon the dynamics of the estimation error is guaranteed to be bounded with a given disturbance attenuation level again an rlmi approach is developed for the state estimation problem finally two simulation examples are exploited to show the effectiveness of the results derived in this paper

variational formulas and cocycle solutions for directed polymer and percolation models we discuss variational formulas for the law of large numbers limits of certain models of motion in a random medium namely the limiting time constant for last passage percolation and the limiting free energy for directed polymers the results are valid for models in arbitrary dimension steps of the admissible paths can be general the environment process is ergodic under spatial translations and the potential accumulated along a path can depend on the environment and the next step of the path the variational formulas come in two types one minimizes over gradient like cocycles and another one maximizes over invariant measures on the space of environments and paths minimizing cocycles can be obtained from busemann functions when these can be proved to exist the results are illustrated through 1 1 dimensional exactly solvable examples periodic examples and polymers in weak disorder

second class particles and cube root asymptotics for hammersley s process we show that for a stationary version of hammersley s process with poisson sources on the positive x axis and poisson sinks on the positive y axis the variance of the length of a longest weakly north east path l t t from 0 0 to t t is equal to 2e t x t where x t is the location of a second class particle at time t this implies that both e t x t and the variance of l t t are of order t2 3 proofs are based on the relation between the flux and the path of a second class particle continuing the approach of cator and groeneboom ann probab 33 2005 879 903

stability analysis for stochastic cohen grossberg neural networks with mixed time delays in this letter the global asymptotic stability analysis problem is considered for a class of stochastic cohen grossberg neural networks with mixed time delays which consist of both the discrete and distributed time delays based on an lyapunov krasovskii functional and the stochastic stability analysis theory a linear matrix inequality lmi approach is developed to derive several sufficient conditions guaranteeing the global asymptotic convergence of the equilibrium point in the mean square it is shown that the addressed stochastic cohen grossberg neural networks with mixed delays are globally asymptotically stable in the mean square if two lmis are feasible where the feasibility of lmis can be readily checked by the matlab lmi toolbox it is also pointed out that the main results comprise some existing results as special cases a numerical example is given to demonstrate the usefulness of the proposed global stability criteria

hammersley s process with sources and sinks we show that for a stationary version of hammersley s process with poisson sources on the positive x axis and poisson sinks on the positive y axis an isolated second class particle located at the origin at time zero moves asymptotically with probability 1 along the characteristic of a conservation equation for hammersley s process this allows us to show that hammersley s process without sinks or sources as defined by aldous and diaconis probab theory related fields 10 1995 199 213 converges locally in distribution to a poisson process a result first proved in aldous and diaconis 1995 by using the ergodic decomposition theorem and a construction of hammersley s process as a one dimensional point process developing as a function of continuous time on the whole real line as a corollary we get the result that el t t t converges to 2 as t where l t t is the length of a longest north east path from 0 0 to t t the proofs of these facts need neither the ergodic decomposition theorem nor the subadditive ergodic theorem we also prove a version of burke s theorem for the stationary process with sources and sinks and briefly discuss the relation of these results with the theory of longest increasing subsequences of random permutations

error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems in this paper we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite horizon optimal control problems of discrete time deterministic nonlinear systems we consider approximation errors in the update equations of both value function and control policy we utilize a new assumption instead of the contraction assumption in discounted optimal control problems we establish the error bounds for approximate value iteration based on a new error condition furthermore we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms it is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions to implement the developed algorithms critic and action neural networks are used to approximate the value function and control policy respectively finally a simulation example is given to demonstrate the effectiveness of the developed algorithms

geodesics and spanning trees for euclidean first passage percolation the metric d q q on the set q of particle locations of a homogeneous poisson process on r d defined as the infimum of i q i q i 1 1 over sequences in q starting with q and ending with q where denotes euclidean distance has nontrivial geodesics when a 1 the cases 1 are the euclidean first passage percolation fpp models introduced earlier by the authors while the geodesics in the case are exactly the paths from the euclidean minimal spanning trees forests of aldous and steele we compare and contrast results and conjectures for these two situations new results for 1 and any d include inequalities on the fluctuation exponents for the metric x 1 2 and for the geodesies 3 4 in strong enough versions to yield conclusions not yet obtained for lattice fpp almost surely every semiinfinite geodesic has an asymptotic direction and every direction has a semiinfinite geodesic from every q for d 2 and 2 further results follow concerning spanning trees of semiinfinite geodesics and related random surfaces

estimator design for discrete time switched neural networks with asynchronous switching and time varying delay this brief deals with the estimator design problem for discrete time switched neural networks with time varying delay one main problem is the asynchronous mode switching between the neuron state and the estimator our goal is to design a mode dependent estimator for the switched neural networks under average dwell time switching such that the estimation error system is exponentially stable with a prescribed l2 gain in the h sense from the noise signal to the estimation error a new lyapunov functional is constructed that may increase during the mismatched switchings new results on the stability and l2 gain analysis are then obtained the admissible estimator gains are computed by solving a set of linear matrix inequalities the relations among the switching law the maximal delay upper bound and the optimal h disturbance attenuation level are established the effectiveness of the proposed design method is finally illustrated by a numerical example

robust control on saturated markov jump systems with missing information in this paper a robust h controller is designed for saturated markov jump systems with uncertainties and time varying transition probabilities the time varying transition probability uncertainty is described as a polytope set stochastic stability is analyzed for the underlying systems by lyapunov function approach and a sufficient condition is derived to design controllers such that the resulting closed loop system is stochastically stable and a prescribed h performance is also achieved furthermore the attraction domain of this markov jump system is estimated and evaluated a simulation example is given to show the effectiveness of the developed techniques

density and uniqueness in percolation two results on site percolation on thed dimensional lattice d 1 arbitrary are presented in the first theorem we show that for stationary underlying probability measures each infinite cluster has a well defined density with probability one the second theorem states that if in addition the probability measure satisfies the finite energy condition of newman and schulman then there can be at most one infinite cluster with probability one the simple arguments extend to a broad class of finite dimensional models including bond percolation and regular lattices

geodesics of random riemannian metrics we analyze the disordered riemannian geometry resulting from random perturbations of the euclidean metric we focus on geodesics the paths traced out by a particle traveling in this quenched random environment by taking the point of the view of the particle we show that the law of its observed environment is absolutely continuous with respect to the law of the random metric and we provide an explicit form for its radon nikodym derivative we use this result to prove a local markov property along an unbounded geodesic demonstrating that it eventually encounters any type of geometric phenomenon we also develop in this paper some general results on conditional gaussian measures our main theorem states that a geodesic chosen with random initial conditions chosen independently of the metric is almost surely not minimizing to demonstrate this we show that a minimizing geodesic is guaranteed to eventually pass over a certain bump surface which locally has constant positive curvature by using jacobi fields we show that this is sufficient to destabilize the minimizing property

h_ infty state estimation for complex networks with uncertain inner coupling and incomplete measurements in this paper the h state estimation problem is investigated for a class of complex networks with uncertain coupling strength and incomplete measurements with the aid of the interval matrix approach we make the first attempt to characterize the uncertainties entering into the inner coupling matrix the incomplete measurements under consideration include sensor saturations quantization and missing measurements all of which are assumed to occur randomly by introducing a stochastic kronecker delta function these incomplete measurements are described in a unified way and a novel measurement model is proposed to account for these phenomena occurring with individual probability with the measurement model a set of h state estimators is designed such that for all admissible incomplete measurements as well as the uncertain coupling strength the estimation error dynamics is exponentially mean square stable and the h performance requirement is satisfied the characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem that can be easily solved using the semidefinite program method finally a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach

geodesics and the competition interface for the corner growth model we study the directed last passage percolation model on the planar integer lattice with nearest neighbor steps and general i i d weights on the vertices outside the class of exactly solvable models in georgiou et al probab theory relat fields 2016 doi 10 1007 s00440 016 0729 x we constructed stationary cocycles and busemann functions for this model using these objects we prove new results on the competition interface on existence uniqueness and coalescence of directional semi infinite geodesics and on nonexistence of doubly infinite geodesics

filtering for discrete fuzzy stochastic systems with sensor nonlinearities this paper deals with the filtering problem for discrete time fuzzy stochastic systems with sensor nonlinearities there exist time varying parameter uncertainties and random noise depending on state and external disturbance the characteristic of nonlinear sensor is handled by a decomposition method by means of the parallel distributed compensation technique the design method of the robust h filter is presented sufficient conditions for the stochastic stability of the filtering error systems are derived such that the filter parameters can be explicitly obtained simulation results are given to illustrate the proposed method

stationary cocycles and busemann functions for the corner growth model we study the directed last passage percolation model on the planar square lattice with nearest neighbor steps and general i i d weights on the vertices outside of the class of exactly solvable models stationary cocycles are constructed for this percolation model from queueing fixed points these cocycles serve as boundary conditions for stationary last passage percolation solve variational formulas that characterize limit shapes and yield existence of busemann functions in directions where the shape has some regularity in a sequel to this paper the cocycles are used to prove results about semi infinite geodesics and the competition interface

brief paper mode dependent h filtering for discrete time markovian jump linear systems with partly unknown transition probabilities in this paper the problem of h filtering for a class of discrete time markovian jump linear systems mjls with partly unknown transition probabilities is investigated the considered systems are more general which cover the mjls with completely known and completely unknown transition probabilities as two special cases a mode dependent full order filter is constructed and the bounded real lemma brl for the resulting filtering error system is derived via lmi formulation then an improved version of the brl is further given by introducing additional slack matrix variables to eliminate the cross coupling between system matrices and lyapunov matrices among different operation modes finally the existence criterion of the desired filter is obtained such that the corresponding filtering error system is stochastically stable with a guaranteed h performance index a numerical example is presented to illustrate the effectiveness and potential of the developed theoretical results

brief paper finite time control of discrete time linear systems analysis and design conditions in this paper we deal with some finite time control problems for discrete time time varying linear systems first we provide necessary and sufficient conditions for finite time stability these conditions require either the computation of the state transition matrix of the system or the solution of a certain difference lyapunov inequality then we address the design problem the proposed conditions allow us to find output feedback controllers which stabilize the closed loop system in the finite time sense all these conditions can be expressed in terms of lmis and therefore are numerically tractable as shown in the example included in the paper

ratios of partition functions for the log gamma polymer we introduce a random walk in random environment associated to an underlying directed polymer model in 1 1 dimensions this walk is the positive temperature counterpart of the competition in terface of percolation and arises as the limit of quenched polymer measures we prove this limit for the exactly solvable log gamma polymer as a consequence of almost sure limits of ratios of parti tion functions these limits of ratios give the busemann functions of the log gamma polymer and furnish centered cocycles that solve a variational formula for the limiting free energy limits of ratios of point to point and point to line partition functions manifest a duality between tilt and velocity that comes from quenched large deviations under polymer measures in the log gamma case we identify a fam ily of ergodic invariant distributions for the random walk in random environment

state estimation and sliding mode control for semi markovian jump systems with mismatched uncertainties this paper is concerned with the state estimation and sliding mode control problems for phase type semi markovian jump systems using a supplementary variable technique and a plant transformation a finite phase type semi markov process has been transformed into a finite markov chain which is called its associated markov chain as a result phase type semi markovian jump systems can be equivalently expressed as its associated markovian jump systems a sliding surface is then constructed and a sliding mode controller is synthesized to ensure that the associated markovian jump systems satisfy the reaching condition moreover an observer based sliding mode control problem is investigated sufficient conditions are established for the solvability of the desired observer two numerical examples are presented to show the effectiveness of the proposed design techniques

exponential rm h _ infty synchronization of general discrete time chaotic neural networks with or without time delays this brief studies exponential h synchronization of a class of general discrete time chaotic neural networks with external disturbance on the basis of the drive response concept and h control theory and using lyapunov krasovskii or lyapunov functional state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays but also reduce the effect of external disturbance on the synchronization error to a minimal h norm constraint the proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities most discrete time chaotic systems with or without time delays such as hopfield neural networks cellular neural networks bidirectional associative memory networks recurrent multilayer perceptrons cohen grossberg neural networks chua s circuits etc can be transformed into this general chaotic neural network to be h synchronization controller designed in a unified way finally some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods

periodic coxeter matrices abstract let a kq i be a finite dimensional triangular k algebra consider the cartan matrix c a and the coxeter matrix a c a t c a let t det t id a be the coxeter polynomial of a we study conditions on spec a in order that a is a periodic matrix we show that in case a is periodic then the euler quadratic form q a x xc a t x t is non negative and q a 0 if and only if 1 spec a

a comprehensive review of stability analysis of continuous time recurrent neural networks stability problems of continuous time recurrent neural networks have been extensively studied and many papers have been published in the literature the purpose of this paper is to provide a comprehensive review of the research on stability of continuous time recurrent neural networks including hopfield neural networks cohen grossberg neural networks and related models since time delay is inevitable in practice stability results of recurrent neural networks with different classes of time delays are reviewed in detail for the case of delay dependent stability the results on how to deal with the constant variable delay in recurrent neural networks are summarized the relationship among stability results in different forms such as algebraic inequality forms m matrix forms linear matrix inequality forms and lyapunov diagonal stability forms is discussed and compared some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed concluding remarks and future directions of stability analysis of recurrent neural networks are given

finite time state estimation for coupled markovian neural networks with sensor nonlinearities this paper investigates the issue of finite time state estimation for coupled markovian neural networks subject to sensor nonlinearities where the markov chain with partially unknown transition probabilities is considered a luenberger type state estimator is proposed based on incomplete measurements and the estimation error system is derived by using the kronecker product by using the lyapunov method sufficient conditions are established which guarantee that the estimation error system is stochastically finite time bounded and stochastically finite time stable respectively then the estimator gains are obtained via solving a set of coupled linear matrix inequalities finally a numerical example is given to illustrate the effectiveness of the proposed new design method

hammersley s interacting particle process and longest increasing subsequences in a famous paper 8 hammersley investigated the lengthl n of the longest increasing subsequence of a randomn permutation implicit in that paper is a certain one dimensional continuous space interacting particle process by studying a hydrodynamical limit for hammersley s process we show by fairly soft arguments that limn 1 2 el n 2 this is a known result but previous proofs 14 11 relied on hard analysis of combinatorial asymptotics

robust state estimation for uncertain neural networks with time varying delay the robust state estimation problem for a class of uncertain neural networks with time varying delay is studied in this paper the parameter uncertainties are assumed to be norm bounded based on a new bounding technique a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks the criterion is dependent on the size of the time varying delay and on the size of the time derivative of the time varying delay it is shown that the design of the robust state estimator for such neural networks can be achieved by solving a linear matrix inequality lmi which can be easily facilitated by using some standard numerical packages finally two simulation examples are given to demonstrate the effectiveness of the developed approach

state estimation for delayed neural networks in this letter the state estimation problem is studied for neural networks with time varying delays the interconnection matrix and the activation functions are assumed to be norm bounded the problem addressed 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 an effective linear matrix inequality approach is developed to solve the neuron state estimation problem in particular we derive the conditions for the existence of the desired estimators for the delayed neural networks we also parameterize the explicit expression of the set of desired estimators in terms of linear matrix inequalities lmis finally it is shown that the main results can be easily extended to cope with the traditional stability analysis problem for delayed neural networks numerical examples are included to illustrate the applicability of the proposed design method

limiting distributions for a polynuclear growth model with external sources the purpose of this paper is to investigate the limiting distribution functions for a polynuclear growth model with two external sources which was considered by prahofer and spohn depending on the strength of the sources the limiting distribution functions are either the tracy widom functions of random matrix theory or a new explicit function which has the special property that its mean is zero moreover we obtain transition functions between pairs of the above distribution functions in suitably scaled limits there are also similar results for a discrete totally asymmetric exclusion process