combination of fuzzy numbers representing expert opinions abstract expert opinions or imprecise estimates of a physical variable are expressed as fuzzy numbers and five techniques for combining these numbers into a single fuzzy number estimate are developed seven characteristics of the combination technique are defined namely agreement preservation order independence transformation variance possibility conservation possibility interval conservation relationship between uncertainty of individual estimates and overall uncertainty and desirability of resultant estimate the five techniques listed in increasing order of preference are 1 crisp weighting 2 fuzzy weighting 3 minimal fuzzy extension 4 convex fuzzy extension and 5 mixed linear extension an example of estimating nitrate concentration in ground water illustrates the approach the cases of equally versus unequally reliable estimates are distinguished and guidelines for choice of combination technique are provided

stability analysis of markovian jump stochastic bam neural networks with impulse control and mixed time delays this paper discusses the issue of stability analysis for a class of impulsive stochastic bidirectional associative memory neural networks with both markovian jump parameters and mixed time delays the jumping parameters are modeled as a continuous time discrete state markov chain based on a novel lyapunov krasovskii functional the generalized ito s formula mathematical induction and stochastic analysis theory a linear matrix inequality approach is developed to derive some novel sufficient conditions that guarantee the exponential stability in the mean square of the equilibrium point at the same time we also investigate the robustly exponential stability in the mean square of the corresponding system with unknown parameters it should be mentioned that our stability results are delay dependent which depend on not only the upper bounds of time delays but also their lower bounds moreover the derivatives of time delays are not necessarily zero or smaller than one since several free matrices are introduced in our results consequently the results obtained in this paper are not only less conservative but also generalize and improve many earlier results finally two numerical examples and their simulations are provided to show the effectiveness of the theoretical results

reliable asynchronous sampled data filtering of t s fuzzy uncertain delayed neural networks with stochastic switched topologies abstract this paper investigates the issue of the reliable asynchronous sampled data filtering of takagi sugeno t s fuzzy delayed neural networks with stochastic intermittent faults randomly occurring time varying parameters uncertainties and controller gain fluctuation the asynchronous phenomenon occurs between the system modes and controller modes first in order to reduce the utilization rate of communication bandwidth a novel alterable sampled data terminal method is considered via variable sampling rates second based on the fuzzy model based control approach improved reciprocally convex inequality and new parameter time dependent discontinuous lyapunov approach several relaxed conditions are derived and compared with the existing work third the intermittent fault tolerance scheme is also taken into fully account in designing a reliable asynchronous sampled data controller which ensures such that the resultant neural networks is asymptotically stable finally two numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results

estimation of the domain of attraction for discrete time linear impulsive control systems with input saturation abstract this paper discusses the domain of attraction and stability analysis for discrete time linear impulsive systems with input saturation the first goal is to give a stable condition for the system the second is to present a strategy to find an inner estimate of the domain of attraction via establishing lyapunov functions and using other mathematical methods finally we present several examples to illustrate the validity of the conditions we have derived

stability analysis of stochastic delay differential equations with levy noise abstract this paper is concerned with p th moment exponential stability problem for a class of stochastic delay differential equations driven by levy processes several new stability theorems are obtained by developing a method proof by contradiction moreover the results are applied to investigate the p th moment exponential stability of stochastic neural networks with levy noise in particular the time varying delay in our results is not required to be differentiable even not continuous the obtained results improve greatly some previous works given in the literature in particular our method can easily correct the incorrect proofs appeared in two recent papers finally two examples are provided to show the effectiveness of the theoretical results

stochastic stability of neutral type markovian jumping bam neural networks with time varying delays abstract in this paper stochastic stability of neutral type markovian jumping bidirectional associative memory bam neural networks is investigated the jumping parameters are modeled as a continuous time discrete state markov chain the activation functions are supposed to be bounded and globally lipschitz continuous furthermore based on the lyapunov krasovskii functional a linear matrix inequality lmi approach is developed to establish sufficient conditions and novel delay dependent conditions are established for the stochastic asymptotic stability of markovian jumping bam neural networks the condition is presented in terms of linear matrix inequalities lmis which can be easily checked by using matlab lmi toolbox finally numerical examples are provided to show the effectiveness of the main results

generalization bounds of erm based learning processes for continuous time markov chains many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed i i d however the assumption of i i d samples is not suitable for practical application to problems in which samples are time dependent in this paper we are mainly concerned with the empirical risk minimization erm based learning process for time dependent samples drawn from a continuous time markov chain this learning process covers many kinds of practical applications e g the prediction for a time series and the estimation of channel state information thus it is significant to study its theoretical properties including the generalization bound the asymptotic convergence and the rate of convergence it is noteworthy that since samples are time dependent in this learning process the concerns of this paper cannot at least straightforwardly be addressed by existing methods developed under the sample i i d assumption we first develop a deviation inequality for a sequence of time dependent samples drawn from a continuous time markov chain and present a symmetrization inequality for such a sequence by using the resultant deviation inequality and symmetrization inequality we then obtain the generalization bounds of the erm based learning process for time dependent samples drawn from a continuous time markov chain finally based on the resultant generalization bounds we analyze the asymptotic convergence and the rate of convergence of the learning process

stability of markovian jump generalized neural networks with interval time varying delays this paper examines the problem of asymptotic stability for markovian jump generalized neural networks with interval time varying delays markovian jump parameters are modeled as a continuous time and finite state markov chain by constructing a suitable lyapunov krasovskii functional lkf and using the linear matrix inequality lmi formulation new delay dependent stability conditions are established to ascertain the mean square asymptotic stability result of the equilibrium point the reciprocally convex combination technique jensen s inequality and the wirtinger based double integral inequality are used to handle single and double integral terms in the time derivative of the lkf the developed results are represented by the lmi the effectiveness and advantages of the new design method are explained using five numerical examples

almost sure exponential stability of numerical solutions for stochastic delay hopfield neural networks with jumps abstract in this paper we main investigate the almost sure exponential stability of stochastic delay hopfield neural networks with jumps on numerical solutions the methods we used are euler approach and backward euler approach by giving some conditions of theoretical significance we verify that not only euler approach but also backward euler approach is almost sure exponential stability however the range of application of euler approach is smaller than that of backward euler approach moreover our main research tool is the discrete semimartingale convergence theorem lastly we give an example as illustration

hopf bifurcation of an n 1 neuron bidirectional associative memory neural network model with delays recent studies on hopf bifurcations of neural networks with delays are confined to simplified neural network models consisting of only two three four five or six neurons it is well known that neural networks are complex and large scale nonlinear dynamical systems so the dynamics of the delayed neural networks are very rich and complicated although discussing the dynamics of networks with a few neurons may help us to understand large scale networks there are inevitably some complicated problems that may be overlooked if simplified networks are carried over to large scale networks in this paper a general delayed bidirectional associative memory neural network model with n 1 neurons is considered by analyzing the associated characteristic equation the local stability of the trivial steady state is examined and then the existence of the hopf bifurcation at the trivial steady state is established by applying the normal form theory and the center manifold reduction explicit formulae are derived to determine the direction and stability of the bifurcating periodic solution furthermore the paper highlights situations where the hopf bifurcations are particularly critical in the sense that the amplitude and the period of oscillations are very sensitive to errors due to tolerances in the implementation of neuron interconnections it is shown that the sensitivity is crucially dependent on the delay and also significantly influenced by the feature of the number of neurons numerical simulations are carried out to illustrate the main results

delay dependent stability for uncertain stochastic neural networks with time varying delay abstract this paper is concerned with the robust stability analysis problem for uncertain stochastic neural networks with time varying delay the parameter uncertainties are assumed to be norm bounded by defining a new lyapunov krasovskii functional the restrictions such as the time varying delay was required to be differentiable and its derivative was strictly smaller than one are removed based on the linear matrix inequality approach delay dependent stability criteria are obtained such that for all admissible uncertainties the stochastic neural network is globally asymptotically stable in the mean square two slack variables are introduced into the obtained stability criteria to reduce the conservatism finally a numerical example is given to illustrate the effectiveness of the developed method

mean square stability of two classes of theta methods for numerical computation and simulation of delayed stochastic hopfield neural networks abstract recently the investigation on the stability of the numerical solutions to delayed stochastic differential equations has received an increasing attention but there has been little work on the stability analysis of the numerical solutions to delayed stochastic hopfield neural networks dshnns so far the aim in this paper is to study the mean square exponential stability of the split step theta sst method and the stochastic linear theta slt method for the underlying model it is proved that for any 0 1 2 there exists a constant 0 depending on such that the numerical schemes produced by the sst method and the slt method are mean square exponentially stable for 0 under the same assumptions as those to guarantee the mean square exponential stability of the underlying continuous model for the case 1 2 1 we show the same stability conclusion for all 0 to carry out the required conclusion a novel technique for the stability analysis of discrete numerical schemes with multi time delays namely the weighted sum lyapunov functional method is proposed finally a numerical example is given to illustrate the application of the suggested methods and to verify the stability conclusions obtained

exponential convergence and lagrange stability for impulsive cohen grossberg neural networks with time varying delays in this paper the problem on exponential convergence and lagrange exponential stability for a class of delayed cohen grossberg neural networks with impulses effects is investigated to this end a new delay impulsive differential inequality is established which improves and generalizes previously known criteria by using the new inequality and coupling with the lyapunov method several sufficient conditions are derived to guarantee the global exponential stability in lagrange sense and exponential convergence of the state variables of the discussed delayed cohen grossberg neural networks with impulses effects meanwhile the framework of the exponential convergence ball in the state space with a pre specified convergence rate is also given here the existence and uniqueness of the equilibrium points need not to be considered finally some numerical examples with simulation show the effectiveness of the obtained results we discuss lagrange stability for impulsive cohen grossberg neural networks we establish a new delay impulsive differential inequality some easily verified conditions of lagrange exponential stability are obtained giving out the detail estimations of the exponential convergence ball the results here generalize and improve the earlier publications

dynamic stability analysis of stochastic fractional order memristor fuzzy bam neural networks with delay and leakage terms abstract this paper deals with the uniform stability in mean square of stochastic fractional order memristor fuzzy bam neural networks with delay and leakage terms by employing the ideas of cauchy schwartz inequality burkholder davis gundy inequality analysis technique some sufficient conditions are derived to ensure the uniform stability in mean square of such networks the existence uniqueness and stability of its equilibrium point are also showed two different fractional order derivatives between the u layer and v layer are taken into consideration synchronously with fractional order 1 2

new results on stability of discrete time systems with time varying state delay this note is concerned with the stability analysis of discrete time systems with time varying state delay by defining new lyapunov functions and by making use of novel techniques to achieve delay dependence several new conditions are obtained for the asymptotic stability of these systems the merit of the proposed conditions lies in their less conservativeness which is achieved by circumventing the utilization of some bounding inequalities for cross products between two vectors and by paying careful attention to the subtle difference between the terms sigmam k dk k 1 middot and sigma m k dm k 1 middot which is largely ignored in the existing literature these conditions are shown via several examples to be much less conservative than some existing result

marginalization and linear opinion pools abstract suppose a decision maker has asked a group of experts to assess subjective probability distributions over some space and he wishes to find a consensus probability distribution as a function of these the assumption that finding the consensus distribution commutes with marginalization of the distributions implies that the consensus must be found using a linear opinion pool weighted average provided the space being considered contains three or more points some of the consequences of this result are discussed

finely harmonic functions with finite dirichlet integral with respect to the green measure we consider finely harmonic functions h on a fine greenian domain v c rd with finite dirichlet integral wrt gm i e f ivh y 2 g x y dm y oo for x c v where m denotes the lebesgue measure g x y the green function we use brownian motion and stochastic calculus to prove that such functions h always have boundary values h along a a brownian paths this partially extends results by doob brelot and godefroid who considered ordinary harmonic functions with finite dirichlet integral wrt m and green lines instead of brownian paths as a consequence of theorem 1 we obtain several properties equivalent to one of these being that h is the harmonic extension to v of a random boundary function h of a certain type i e h x ex h for all x c v another application is that the polar sets are removable singularity sets for finely harmonic functions satisfying this is in contrast with the situation for finely harmonic functions with finite dirichlet integral wrt m

stability analysis of markovian jumping stochastic cohen grossberg neural networks with mixed time delays in this letter the global asymptotical stability analysis problem is considered for a class of markovian jumping stochastic cohen grossberg neural networks cgnns with mixed delays including discrete delays and distributed delays an alternative delay dependent stability analysis result is established based on the linear matrix inequality lmi technique which can easily be checked by utilizing the numerically efficient matlab lmi toolbox neither system transformation nor free weight matrix via newton leibniz formula is required two numerical examples are included to show the effectiveness of the result

global exponential stability of impulsive integro differential equation abstract in this paper an impulsive integro differential equation is considered by establishing an integro differential inequality with impulsive initial conditions and using the properties of m cone and eigenspace of the spectral radius of nonnegative matrices some new sufficient conditions for global exponential stability of impulsive integro differential equation are obtained the results extend and improve the earlier publications an example is given to demonstrate the effectiveness of the theory

stabilization of multi group models with multiple dispersal and stochastic perturbation via feedback control based on discrete time state observations abstract this paper focuses on the multi group models with multiple dispersal and stochastic perturbation mgmds the effect of both multiple dispersal among groups and stochastic perturbation are taken into consideration the stabilization of mgmds is investigated via feedback control based on the discrete time state observations in addition a systematic method is given to construct a global lyapunov function for mgmds by means of graph theory and lyapunov method some sufficient conditions are obtained to ensure the stabilization in the sense of mean square asymptotical stability an upper bound of the duration between two consecutive state observations is estimated moreover to show the applicability of our results the main theory is employed to a stochastic coupled oscillators finally a numerical example is given to illustrate the effectiveness and feasibility of the theoretical results

existence and uniform stability analysis of fractional order complex valued neural networks with time delays this paper deals with the problem of existence and uniform stability analysis of fractional order complex valued neural networks with constant time delays complex valued recurrent neural networks is an extension of real valued recurrent neural networks that includes complex valued states connection weights or activation functions this paper explains sufficient condition for the existence and uniform stability analysis of such networks three numerical simulations are delineated to substantiate the effectiveness of the theoretical results

dynamical behavior of delayed reaction diffusion hopfield neural networks driven by infinite dimensional wiener processes in this paper we focus on the long time behavior of the mild solution to delayed reaction diffusion hopfield neural networks drdhnns driven by infinite dimensional wiener processes we analyze the existence uniqueness and stability of this system under the local lipschitz function by constructing an appropriate lyapunov krasovskii function and utilizing the semigroup theory some easy to test criteria affecting the well posedness and stability of the networks such as infinite dimensional noise and diffusion effect are obtained the criteria can be used as theoretic guidance to stabilize drdhnns in practical applications when infinite dimensional noise is taken into consideration meanwhile considering the fact that the standard brownian motion is a special case of infinite dimensional wiener process we undertake an analysis of the local lipschitz condition which has a wider range than the global lipschitz condition two samples are given to examine the availability of the results in this paper simulations are also given using the matlab

impulsive control for existence uniqueness and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays in this paper a class of recurrent neural networks with discrete and continuously distributed delays is considered sufficient conditions for the existence uniqueness and global exponential stability of a periodic solution are obtained by using contraction mapping theorem and stability theory on impulsive functional differential equations the proposed method which differs from the existing results in the literature shows that network models may admit a periodic solution which is globally exponentially stable via proper impulsive control strategies even if it is originally unstable or divergent two numerical examples and their computer simulations are offered to show the effectiveness of our new results

stability analysis of impulsive stochastic cohen grossberg neural networks driven by g brownian motion this paper is devoted to study the stability of a class of impulsive stochastic cohen grossberg neural networks driven by g brownian motion by means of g lyapunov function borel cantelli lemma an

note on the aggregation of individual probability estimates this note considers the problem of aggregating individual probability estimates of an event to obtain a group estimate norman dalkey has argued that no rigorous theory of probability aggregation is possible for the following reasons 1 there is no consistent way of aggregating individual probability estimates dalkey s impossibility theorem but dalkey s impossibility theorem makes what we call the context free assumption namely that the group estimate of an event s probability is a function only of the estimates of the individuals and in particular is not dependent on the event in question we argue that the context free assumption is unreasonable and present several known aggregation methods which don t make it 2 although there is a bayesian approach to the problem it requires that the estimates of all but at most one of the experts be independent of the event in question to make this argument dalkey assumed both a1 the individual estimates are independent random variables and a2 given the occurrence or nonoccurrence of the event the individual estimates are independent random variables this paper presents an alternate derivation of the result abstracting from dalkey s context the point is that dalkey s result is simply a consequence of assuming both a1 and a2 it is unreasonable to assume both a1 and a2 and hence dalkey s result does not discredit the bayesian approach thus this paper resolves some theoretical objections to the possibility of a rigorous theory of aggregation furthermore it clarifies the nature of the context free assumption and the independence assumptions a1 and a2

global asymptotic stability of bam neural networks with mixed delays and impulses in this paper bam neural networks with mixed delays and impulses are considered a new set of sufficient conditions are derived by constructing suitable lyapunov functional with matrix theory for the global asymptotic stability of bam neural networks with mixed delays and impulses moreover an example is also provided to illustrate the effectiveness of the results

robust exponential stability of markovian jump impulsive stochastic cohen grossberg neural networks with mixed time delays this paper is concerned with the problem of exponential stability for a class of markovian jump impulsive stochastic cohen grossberg neural networks with mixed time delays and known or unknown parameters the jumping parameters are determined by a continuous time discrete state markov chain and the mixed time delays under consideration comprise both time varying delays and continuously distributed delays to the best of the authors knowledge till now the exponential stability problem for this class of generalized neural networks has not yet been solved since continuously distributed delays are considered in this paper the main objective of this paper is to fill this gap by constructing a novel lyapunov krasovskii functional and using some new approaches and techniques several novel sufficient conditions are obtained to ensure the exponential stability of the trivial solution in the mean square the results presented in this paper generalize and improve many known results finally two numerical examples and their simulations are given to show the effectiveness of the theoretical results

a conflict between two axioms for combining subjective distributions with the development of multi agent statistical decision theory weerahandi and zidek 1981 1983 considerable attention has recently been paid to the problem of combining the diverse beliefs of a group of individuals more specifically if n 2 bayesians formulate their opinions about some quantity 0 in terms of subjective probability densities fi fn on e the parameter space a consensual density t fi fn is desired which could then be used in a conventional uni bayesian analysis owing in part to the complexity of existing solution schemes based on the bayesian inferential framework morris 1974 1977 french 1980 various axioms have been proposed in the literature which purport to embrace the minimal requirements that a pooling formula t should satisfy this note points out a serious incompatibility between two such conditions which are often individually imposed on pooling operators when the two are used simultaneously the resulting procedure is seen to depend on the opinion of a single expert

relaxed stability criteria for neural networks with time varying delay using extended secondary delay partitioning and equivalent reciprocal convex combination techniques this article investigates global asymptotic stability for neural networks nns with time varying delay which is differentiable and uniformly bounded and the delay derivative exists and is upper bounded first we propose the extended secondary delay partitioning technique to construct the novel lyapunov krasovskii functional where both single integral and double integral state variables are considered while the single integral ones are only solved by the traditional secondary delay partitioning second a novel free weight matrix equality fwme is presented to resolve the reciprocal convex combination problem equivalently and directly without schur complement which eliminates the need of positive definite matrices and is less conservative and restrictive compared with various improved reciprocal convex inequalities furthermore by the present extended secondary delay partitioning equivalent reciprocal convex combination technique and bessel legendre inequality two different relaxed sufficient conditions ensuring global asymptotic stability for nns are obtained for time varying delays respectively with unknown and known lower bounds of the delay derivative finally two examples are given to illustrate the superiority and effectiveness of the presented method

further results on exponential stability for impulsive switched nonlinear time delay systems with delayed impulse effects this paper is concerned with the problem of exponential stability for a class of impulsive switched nonlinear time delay systems with delayed impulse effects by using the multiple lyapunov krasovskii functionals technique some exponential stability criteria are obtained respectively for two kinds of impulsive signals destabilizing impulsive signal and stabilizing impulsive signal the derived results not only characterise the effects of delayed impulse time delay and switching on nonlinear systems but also remove some restriction conditions furthermore a more precise bound of system state is given compared with existing results on related problems the obtained results are less conservative three examples are provided to illustrate the effectiveness and the generality of the proposed results

dynamic analysis of stochastic cohen grossberg neural networks with time delays abstract in this paper we discuss stochastic cohen grossberg neural networks with time delays by constructing suitable lyapunov functional and employing the semimartingale convergence theorem we give some sufficient criteria ensuring almost sure exponential stability of the networks the results obtained are helpful to design stability of networks when stochastic noise is taken into consideration

stability in delayed cohen grossberg neural networks lmi optimization approach in this paper stability problems are further discussed for a class of delayed cohen grossberg neural networks on the basis of the linear matrix inequality lmi optimization approach and also the lyapunov krasovskii functional method combined with the halanay inequality technique several new sufficient criteria are given for ascertaining the global asymptotic stability and exponential stability of the equilibrium point for this system the proposed results are less restrictive than those given in the earlier literature and are easier to verify in practice in addition four examples and their numerical simulations are given two of them are used to demonstrate the effectiveness of the proposed results and another two show that there exist stable bifurcating periodic solutions if the given conditions do not hold

trace operator and the dirichlet problem for elliptic equations on arbitrary bounded open sets abstract we consider the dirichlet problem and the weak dirichlet problem on a general possibly nonregular bounded domain for elliptic linear equation with uniformly elliptic divergence form operator we investigate carefully the relationship between weak integral probabilistic soft and the perron wiener brelot solutions of the problem we also study the relationship between solutions of the dirichlet problem and weak dirichlet problem to this end we extend the usual notion of the trace operator and consider the dirichlet problem with martin boundary

global asymptotic stability and stabilization of neural networks with general noise neural networks nns in the stochastic environment were widely modeled as stochastic differential equations which were driven by white noise such as brown or wiener process in the existing papers however they are not necessarily the best models to describe dynamic characters of nns disturbed by nonwhite noise in some specific situations in this paper general noise disturbance which may be nonwhite is introduced to nns since nns with nonwhite noise cannot be described by ito integral equation a novel modeling method of stochastic nns is utilized by a framework in light of random field approach and lyapunov theory the global asymptotic stability and stabilization in probability or in the mean square of nns with general noise are analyzed respectively criteria for the concerned systems based on linear matrix inequality are proposed some examples are given to illustrate the effectiveness of the obtained results

stochastic stability of impulsive bam neural networks with time delays the problem of stability analysis of stochastic bam neural networks with time delays and impulse effects is investigated in this paper using the lyapunov technique and the generalized hanalay inequality we characterize theoretically the aggregated effects of impulse and stability properties of the impulse free version the present approaches allow us to estimate the feasible upper bounds of impulse strengths and can also extend to the more general impulsive nonlinear systems with delays

stability of artificial neural networks with impulses sufficient conditions are obtained for the existence and asymptotic stability of a unique equilibrium of a hopfield type neural network with lipschitzian activation functions without assuming their boundedness monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time both the continuous time and their corresponding discrete time networks are considered the sufficient conditions of the discrete time network do not restrict the step size appearing in the discretization process and these conditions approach as the step size tends to zero those of the conditions of the continuous time networks the sufficient conditions are in terms of the parameters of the network only and are easy to verify also when the impulsive jumps are absent the results reduce to those of the non impulsive systems

exponential stability of stochastic complex networks with multi weights based on graph theory abstract in this paper a novel approach to exponential stability of stochastic complex networks with multi weights is investigated by means of the graph theoretical method new sufficient conditions are provided to ascertain the moment exponential stability and almost surely exponential stability of stochastic complex networks with multiple weights it is noted that our stability results are closely related with multi weights and the intensity of stochastic disturbance numerical simulations are also presented to substantiate the theoretical results

characterization of externally bayesian pooling operators on donne une caracterisation de toutes les formules de groupement externes bayesiennes et on donne des conditions sous lesquelles l opinion d un groupe sera proportionnelle a la moyenne geometrique des densites individuelles

stability analysis of impulsive stochastic cohen grossberg neural networks with mixed time delays abstract in this paper the problem of stability analysis for a class of impulsive stochastic cohen grossberg neural networks with mixed delays is considered the mixed time delays comprise both the time varying and infinite distributed delays by employing a combination of the m matrix theory and stochastic analysis technique a sufficient condition is obtained to ensure the existence uniqueness and exponential p stability of the equilibrium point for the addressed impulsive stochastic cohen grossberg neural network with mixed delays the proposed method which does not make use of the lyapunov functional is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and or distributed delays we then extend our main results to the case where the parameters contain interval uncertainties moreover the exponential convergence rate index is estimated which depends on the system parameters an example is given to show the effectiveness of the obtained results

finite time stability analysis for markovian jump memristive neural networks with partly unknown transition probabilities this paper is concerned with the finite time stochastically stability ftss analysis of markovian jump memristive neural networks with partly unknown transition probabilities in the neural networks there exist a group of modes determined by markov chain and thus the markovian jump was taken into consideration and the concept of ftss is first introduced for the memristive model by introducing a markov switching lyapunov functional and stochastic analysis theory an ftss test procedure is proposed from which we can conclude that the settling time function is a stochastic variable and its expectation is finite the system under consideration is quite general since it contains completely known and completely unknown transition probabilities as two special cases more importantly a nonlinear measure method was introduced to verify the uniqueness of the equilibrium point compared with the fixed point theorem that has been widely used in the existing results this method is more easy to implement besides the delay interval was divided into four subintervals which make full use of the information of the subsystems upper bounds of the time varying delays finally the effectiveness and superiority of the proposed method is demonstrated by two simulation examples

moment exponential stability of stochastic delay systems with delayed impulse effects at random times and applications in the stabilisation of stochastic neural networks in this paper we investigate the exponential stability for a class of impulsive stochastic functional differential equations with delayed impulse effects the impulse instants considered in this p

design of state estimator for bidirectional associative memory neural networks with leakage delays this paper considers the issue of state estimation for a class of bidirectional associative memory bam neural networks more precisely the bam model is considered with mixed delays which includes a constant delay in the leakage term time varying discrete delay and constant distributed delay by constructing a novel lyapunov krasovskii functional lkf together with free weighting matrix technique a new delay dependent sufficient condition is derived to estimate the neuron states through available output measurements such that for all admissible delay bounds the resulting estimation error system is globally asymptotically stable also it is assumed that the derivative of time delay is not necessarily zero or less than one further the derived conditions are formulated in terms of a set of linear matrix inequalities lmis which can be easily solved by using some standard numerical packages finally a numerical example with simulation result is presented to show the effectiveness of the proposed theory the result reveals that the leakage delays have a destabilizing influence on the system and they cannot be ignored

a multiplicative formula for aggregating probability assessments this paper presents an axiomatic approach to the problem of aggregating expert assessments of an event s probability into some group probability assessment a multiplicative formula is derived

stochastic stability of delayed neural networks with local impulsive effects in this paper the stability problem is studied for a class of stochastic neural networks nns with local impulsive effects the impulsive effects considered can be not only nonidentical in different dimensions of the system state but also various at distinct impulsive instants hence the impulses here can encompass several typical impulses in nns the aim of this paper is to derive stability criteria such that stochastic nns with local impulsive effects are exponentially stable in mean square by means of the mathematical induction method several easy to check conditions are obtained to ensure the mean square stability of nns three examples are given to show the effectiveness of the proposed stability criterion

combined convex technique on delay dependent stability for delayed neural networks in this brief by employing an improved lyapunov krasovskii functional lkf and combining the reciprocal convex technique with the convex one a new sufficient condition is derived to guarantee a class of delayed neural networks dnns to be globally asymptotically stable since some previously ignored terms can be considered during the estimation of the derivative of lkf a less conservative stability criterion is derived in the forms of linear matrix inequalities whose solvability heavily depends on the information of addressed dnns finally we demonstrate by two numerical examples that our results reduce the conservatism more efficiently than some currently used methods

stability analysis of cohen grossberg neural network with both time varying and continuously distributed delays in this paper the cohen grossberg neural network model with both time varying and continuously distributed delays is considered without assuming both global lipschitz conditions on these activation functions and the differentiability on these time varying delays applying the idea of vector lyapunov function m matrix theory and inequality technique several new sufficient conditions are obtained to ensure the existence uniqueness and global exponential stability of equilibrium point for cohen grossberg neural network with both time varying and continuously distributed delays these results generalize and improve the earlier publications two numerical examples are given to show the effectiveness of the obtained results it is believed that these results are significant and useful for the design and applications of the cohen grossberg neural networks

p moment stability of stochastic differential equations with impulsive jump and markovian switching this paper introduces some new concepts of p moment stability for stochastic differential equations with impulsive jump and markovian switching some stability criteria of p moment stability for stochastic differential equations with impulsive jump and markovian switching are obtained by using liapunov function method an example is also discussed to illustrate the efficiency of the obtained results

existence and global exponential stability of equilibrium for discrete time fuzzy bam neural networks with variable delays and impulses in this paper we study a class of discrete time fuzzy bam neural networks with variable delays and impulses based on m matrix theory and analytic methods some sufficient conditions are established for the existence and global exponential stability of a unique equilibrium moreover the exponential convergence rate index is estimated a numerical example is given to show the effectiveness of the obtained results in particular the simulation figures establish that fuzzy systems do have more advantages than non fuzzy systems

an analytic method for evaluating the performance of aggregation rules for probability densities it is shown how infinite sequences of densities with defined properties can be used to evaluate the expected performance of mathematical aggregation rules for elicited densities the performance of these rules is measured through the average variance calibration and average brier score of the aggregates a general result for the calibration of the arithmetic average of densities from well calibrated independent experts is given arithmetic and geometric aggregation rules are compared using sequences of normal densities sequences are developed that exhibit dependence among experts and lack of calibration the impact of correlation number of experts and degree of calibration on the performance of the aggregation is demonstrated

dynamical behavior of nonautonomous stochastic reaction diffusion neural network models this brief investigates nonautonomous stochastic reaction diffusion neural network models with s type distributed delays first the existence and uniqueness of mild solution are studied under the lipschitz condition without the linear growth condition due to the existence of a nonautonomous reaction diffusion term and the infinite dimensional wiener process the criteria for the well posedness of the models are established based on the evolution system theory then the s type distributed delay which is an infinite delay is handled by the truncation method and sufficient conditions for the global exponential stability are obtained by constructing a simple lyapunov krasovskii functional candidate finally neural network examples and an illustrative example are given to show the applications of the obtained results

improved stability analysis of uncertain neutral type neural networks with leakage delays and impulsive effects this paper focuses on the stability analysis for neural networks of neutral type with leakage delays and impulsive effects the discrete delays are 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 by utilizing the lyapunov functional method jensen s integral inequality and introducing some free weighting matrices some new delay derivative dependent stability criteria are established for the neutral type neural network the obtained stability criteria are stated in terms of linear matrix inequalities finally numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones