h filtering for nonlinear networked systems with randomly occurring distributed delays missing measurements and sensor saturation this paper is concerned with the h filtering problem for a class of networked systems subject to randomly occurring distributed state delays nonlinearities sensor saturation as well as missing measurements via unreliable communication channels the output measurements are affected by sensor saturation which is described by sector nonlinearities the missing measurement is modeled as a linear random variable satisfying the bernoulli distribution and the nonlinearities with the form of global lipschitz cover the well known nonlinear functions the purpose of the addressed problem is to design a full order filter such that in the simultaneous presence of distributed delays randomly occurring nonlinearities sensor saturation and missing measurements the filtering dynamic system is guaranteed to be exponentially mean square stable and the h filtering performance index is achieved a sufficient condition for the solution of the addressed problem is derived and the explicit expression of the desired filter gains is described in terms of the solution to linear matrix inequality lmi finally a numerical example is provided to show the effectiveness of the designed method

finite time synchronization of discontinuous neural networks with delays and mismatched parameters this paper investigates the problem of finite time drive response synchronization for a class of neural networks with discontinuous activations time varying discrete and infinite time distributed delays and mismatched parameters in order to cope with the difficulties induced by discontinuous activations time delays as well as mismatched parameters simultaneously new 1 norm based analytical techniques are developed both state feedback and adaptive controllers with and without the sign function are designed based on differential inclusion theory and lyapunov functional method several sufficient conditions on the finite time synchronization are obtained our results show that the controllers with a sign function can reduce the conservativeness of control gains and the controllers without a sign function can overcome the chattering phenomenon numerical examples are given to show the effectiveness of the theoretical analysis

exponential stability of almost periodic solutions for memristor based neural networks with distributed leakage delays in this letter we deal with a class of memristor based neural networks with distributed leakage delays by applying a new lyapunov function method we obtain some sufficient conditions that ensure the existence uniqueness and global exponential stability of almost periodic solutions of neural networks we apply the results of this solution to prove the existence and stability of periodic solutions for this delayed neural network with periodic coefficients we then provide an example to illustrate the effectiveness of the theoretical results our results are completely new and complement the previous studies chen zeng and jiang 2014 and jiang zeng and chen 2015

global exponential stability of almost periodic solution of delayed neural networks with discontinuous activations in this paper we study the existence uniqueness and stability of almost periodic solution for the class of delayed neural networks the neural network considered in this paper employs the activation functions which are discontinuous monotone increasing and possibly unbounded under a new sufficient condition we prove that the neural network has a unique almost periodic solution which is globally exponentially stable moreover the obtained conclusion is applied to prove the existence and stability of periodic solution or equilibrium point for delayed neural networks with periodic coefficients or constant coefficients we also give some illustrative numerical examples to show the effectiveness of our results

network based h filtering using a logic jumping like trigger this paper is concerned with the network based h filtering for a stochastic system where data transmission from the stochastic system to a filter is completed via a communication network network induced delays packet dropouts and packet disorders are unavoidable due to the use of the network first a logic zero order hold zoh is designed to discard the disordered packets actively the network induced delays and packet dropouts are modeled as an interval time varying delay by decomposing the delay interval into n subintervals uniformly the filter to be designed is modeled as a markov jumping filter with n modes governed by a markov chain in order to work out the transition rate from one mode to another a logic jumping like trigger is embedded into the logic zoh to simulate the switching of the markov process second based on the markov jumping filter model together with a new integral inequality in the stochastic setting a novel bounded real lemma is presented to ensure that the resultant filtering error system is mean exponentially stable with a prescribed h performance then a sufficient condition on the existence of desired markov jumping filters is provided in terms of a set of linear matrix inequalities finally an air vehicle system is employed to show effectiveness of the proposed design method

optimal estimation and control for lossy network stability convergence and performance in this paper we study the problems of optimal estimation and control i e the linear quadratic gaussian lqg control for systems with packet losses but without acknowledgment such acknowledgment is a signal sent by the actuator to inform the estimator of the incidence of control packet losses for such system which is usually called as a user datagram protocol udp like system the optimal estimation is nonlinear and its calculation is time consuming making its corresponding optimal lqg problem complicated we first propose two conditions 1 the sensor has some computation abilities and 2 the control command exerted to the plant is known to the sensor for a udp like system satisfying these two conditions we derive the optimal estimation by constructing the finite and infinite product probability measure spaces for the estimation error covariances eec we give the stability condition for the expected eec and show the existence of a measurable function to which the eec converges in distribution and propose some practical methods to evaluate the estimation performance finally the lqg controllers are derived and the conditions for the mean square stability of the closed loop system are established

monotone viable trajectories for functional differential inclusions this paper is a study on functional differential inclusions with memory which represent the multivalued version of retarded functional differential equations the main result gives a necessary and sufficient equations the main result gives a necessary and sufficient condition ensuring the existence of viable trajectories that means trajectories remaining in a given nonempty closed convex set defined by given constraints the system must satisfy to be viable some motivations for this paper can be found in control theory where f t f t u u u is the set of possible velocities of the system at time t depending on the past history represented by the function and on a control u ranging over a set u of controls other motivations can be found in planning procedures in microeconomics and in biological evolutions where problems with memory do effectively appear in a multivalued version all these models require viability constraints represented by a closed convex set

global convergence of neural networks with mixed time varying delays and discontinuous neuron activations in this paper we investigate the dynamical behavior of a class of delayed neural networks with discontinuous neuron activations and general mixed time delays involving both time varying delays and distributed delays due to the presence of time varying delays and distributed delays the step by step construction of local solutions cannot be applied this difficulty can be overcome by constructing a sequence of solutions to delayed dynamical systems with high slope activations and show that this sequence converges to a desired filippov solution of the discontinuous delayed neural networks we then derive two sets of sufficient conditions for the global exponential stability and convergence of the neural networks in terms of linear matrix inequalities lmis and m matrix properties equivalently some diagonally dominant conditions respectively convergence behavior of both the neuron state and the neuron output are discussed the obtained results extend previous work on global stability of delayed neural networks with lipschitz continuous neuron activations and neural networks with discontinuous neuron activations and only constant delays

growth of correlation in the hopfield model introducing a finite correlation 0 between any two learned patterns others remaining uncorrelated we observe in a numerical simulation that the hopfield model stores these two patterns with correlation f such that f 0 for any loading capacity the patterns are memorized perfectly with f 0 up to 0 05 for finite correlations 0 not exceeding a value c where c decreases continuously to zero at 0 05

delay dependent exponential stability for a class of neural networks with time delays this paper is concerned with the exponential stability of a class of delayed neural networks described by nonlinear delay differential equations of the neutral type in terms of a linear matrix inequality lmi a sufficient condition guaranteeing the existence uniqueness and global exponential stability of an equilibrium point of such a kind of delayed neural networks is proposed this condition is dependent on the size of the time delay which is usually less conservative than delay independent ones the proposed lmi condition can be checked easily by recently developed algorithms solving lmis examples are provided to demonstrate the effectiveness and applicability of the proposed criteria

l_ 2 l_ infty model reduction for switched lpv systems with average dwell time in this note the model reduction problem for a class of discrete time switched linear parameter varying systems under average dwell time switching is investigated a parameterized reduced model is constructed and the corresponding existence conditions of such models are derived via strict lmi formulation the minimal average dwell time among all the subsystems and the desired reduced system are obtained such that the resulting model error system is exponentially stable and has a guaranteed l 2 l infin error performance a numerical example is given to demonstrate the potential and effectiveness of the developed theoretical results

periodic solutions in an inhibitory two neuron network in this paper we consider a delayed network of two neurons with self feedback and interaction described by an all or none threshold function the discontinuity of signal function makes it difficult to apply directly dynamical system we show that the dynamics of the network can be understood in terms of the iterations of a one dimensional map and we obtain the existence and attractivity of periodic solutions moreover because the network is a limiting case of the corresponding smooth system as the parameter tends to infinity the above results can act as the guide to the rich dynamics of the smooth system therefore our results have important significance in both theory and applications

the cauchy problem for continuous fuzzy differential equations abstract we prove a version of the classical peano s theorem for the initial value problem for a fuzzy differential equation in the metric space of normal fuzzy convex sets with distance given by the maximum of the hausdorff distances between level sets

random dilution in a neural network for biased patterns a neural network introduced by tsodyks and feigel man 1988 suitable for the storage of biased patterns is studied in a randomly diluted form coupled evolution equations are derived for the two order parameters needed to describe a configuration near to a stored pattern these equations are studied numerically and are found to exhibit non linear effects such as spiralling trajectories and limit cycles the bifurcations by which the transition to no memory occurs are illustrated it is seen that a nominated pattern may not be within the basin of attraction of the memory fixed point correlated with it the structure of a memory fixed point is investigated and found to be more complicated than a single configuration finally the situation where two patterns are highly correlated is examined and phase boundaries separating the regimes of no memory undistinguishing memory and distinguishing memory are constructed multiple storage of a pattern does not improve its recall without appropriate modification of the thresholding parameter

fuzzy controller design for nonlinear impulsive fuzzy systems with time delay the stabilization problem is investigated for continuous time takagi sugeno t s fuzzy systems with time delay and impulsive effects in this paper some delay independent and delay dependent stabilization approaches are developed for both state feedback and observer based feedback cases which are based on the lyapunov krasovskii functional approach and a parallel distributed compensation scheme the time delay is dealt by lyapunov theory allied with the halanay lemma the restriction on the interval between each impulsive instant and output sampled instant needs not be equal to or less than a constant scalar but it only requires an inequality satisfaction when the impulsive effects vanish the results can be extended to the corresponding sufficient conditions for the cases of a nonlinear continuous time delay system with a state feedback and observer based sampled output feedback finally some examples are given to demonstrate the effectiveness of the proposed method

almost periodic dynamics of a class of delayed neural networks with discontinuous activations we use the concept of the filippov solution to study the dynamics of a class of delayed dynamical systems with discontinuous right hand side which contains the widely studied delayed neural network models with almost periodic self inhibitions interconnection weights and external inputs we prove that diagonal dominant conditions can guarantee the existence and uniqueness of an almost periodic solution as well as its global exponential stability as special cases we derive a series of results on the dynamics of delayed dynamical systems with discontinuous activations and periodic coefficients or constant coefficients respectively from the proof of the existence and uniqueness of the solution we prove that the solution of a delayed dynamical system with high slope activations approximates to the filippov solution of the dynamical system with discontinuous activations

pth moment stability analysis of stochastic recurrent neural networks with time varying delays this paper addresses in great detail the issue of pth moment exponential stability of stochastic recurrent neural networks with time varying delays with the help of the dini derivative of the expectation of v t x t along the solution x t of the model and the technique of halanay type inequality some novel sufficient conditions on pth moment exponential stability of the trivial solution has been established results of the development as presented in this paper are more general than those reported in some previously published papers an example is also given to illustrate that our results are correct and effectiveness

new passivity analysis for neural networks with discrete and distributed delays in this brief the problem of passivity analysis is investigated for a class of uncertain neural networks nns with both discrete and distributed time varying delays by constructing a novel lyapunov functional and utilizing some advanced techniques new delay dependent passivity criteria are established to guarantee the passivity performance of nns essentially different from the available results when estimating the upper bound of the derivative of lyapunov functionals we consider and best utilize the additional useful terms about the distributed delays which leads to less conservative results these criteria are expressed in the form of convex optimization problems which can be efficiently solved via standard numerical software numerical examples are provided to illustrate the effectiveness and less conservatism of the proposed results

artificial neural network approach for solving fuzzy differential equations the current research attempts to offer a novel method for solving fuzzy differential equations with initial conditions based on the use of feed forward neural networks first the fuzzy differential equation is replaced by a system of ordinary differential equations a trial solution of this system is written as a sum of two parts the first part satisfies the initial condition and contains no adjustable parameters the second part involves a feed forward neural network containing adjustable parameters the weights hence by construction the initial condition is satisfied and the network is trained to satisfy the differential equations this method in comparison with existing numerical methods shows that the use of neural networks provides solutions with good generalization and high accuracy the proposed method is illustrated by several examples

brief paper h estimation for discrete time piecewise homogeneous markov jump linear systems this paper concerns the problem of h estimation for a class of markov jump linear systems mjls with time varying transition probabilities tps in discrete time domain the time varying character of tps is considered to be finite piecewise homogeneous and the variations in the finite set are considered to be of two types arbitrary variation and stochastic variation respectively the latter means that the variation is subject to a higher level transition probability matrix the mode dependent and variation dependent h filter is designed such that the resulting closed loop systems are stochastically stable and have a guaranteed h filtering error performance index using the idea in the recent studies of partially unknown tps for the traditional mjls with homogeneous tps a generalized framework covering the two kinds of variations is proposed a numerical example is presented to illustrate the effectiveness and potential of the developed theoretical results

brief paper robust h filtering for a class of uncertain linear systems with time varying delay this paper deals with the problem of delay dependent robust h filtering for uncertain linear systems with time varying delay two cases of time varying delays are fully considered one is the time varying delay being continuous uniformly bounded while the other is the time varying delay being differentiable uniformly bounded with delay derivative bounded by a constant a stable linear filter is designed to ensure that the filtering error system is asymptotically stable with a prescribed level of h noise attenuation based on a new integral inequality delay dependent sufficient conditions for the existence of such a filter are established in terms of linear matrix inequalities lmis through deriving these conditions neither model transformation nor bounding technique for cross terms is employed moreover the relationship between the sufficient conditions for the two cases of time varying delay is disclosed finally two examples are also given to illustrate the effectiveness of the proposed methodology

pair correlated patterns in hopfield model of neural networks we study the retrieval properties of the hopfield model of neural networks when the memorized patterns are statistically correlated in pairs there is a finite correlationk between the memories of each pair but memories of different pairs are uncorrelated the analysis is restricted to the case of an arbitrary but finite number of memories in the thermodynamic limit we find that there are two retrieval regimes for 0 t 1 k the system recognizes the stored patterns and for 1 k t 1 k the system is able to recognize pairs but it is not able to distinguish between its two patterns

pareto optimal strategy for linear stochastic systems with h constraint in finite horizon abstract in this paper we derive conditions for the existence of pareto efficient strategy and pareto solution under h constraint for the linear quadratic lq finite horizon cooperative differential game of stochastic systems with state control and disturbance multiplicative noise firstly we present a stochastic bounded real lemma sbrl with any initial condition for the considered stochastic system next a necessary and sufficient condition for pareto optimal strategy under the h constraint is researched by two cross coupled generalized differential riccati equations gdres which is the key contribution of this paper another contribution is that the pareto solution for every controller is given under the pareto efficient strategy and worst case external disturbance finally a practical example is given to illustrate the effectiveness of our results

existence and stability of almost periodic solutions of hopfield neural networks with continuously distributed delays abstract in this paper the global stability and almost periodicity are investigated for hopfield neural networks with continuously distributed neutral delays some sufficient conditions are obtained for the existence and globally exponential stability of almost periodic solution by employing fixed point theorem and differential inequality techniques the results of this paper are new and they complement the previously known ones finally an example is given to demonstrate the effectiveness of our results

multistability and new attraction basins of almost periodic solutions of delayed neural networks in this paper we investigate multistability of almost periodic solutions of recurrently connected neural networks with delays simply called delayed neural networks we will reveal that under some conditions the space rn can be divided into 2n subsets and in each subset the delayed n neuron neural network has a locally stable almost periodic solution furthermore we also investigate the attraction basins of these almost periodic solutions we reveal that the attraction basin of almost periodic trajectory is larger than the subset where the corresponding almost periodic trajectory is located in addition several numerical simulations are presented to corroborate the theoretical 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

some novel approaches on state estimation of delayed neural networks this paper studies the issue of state estimation for a class of neural networks nns with time varying delay a novel lyapunov krasovskii functional lkf is constructed where triple integral terms are used and a secondary delay partition approach sdpa is employed compared with the existing delay partition approaches the proposed approach can exploit more information on the time delay intervals by taking full advantage of a modified wirtinger s integral inequality mwii improved delay dependent stability criteria are derived which guarantee the existence of desired state estimator for delayed neural networks dnns a better estimator gain matrix is obtained in terms of the solution of linear matrix inequalities lmis in addition a new activation function dividing method is developed by bringing in some adjustable parameters three numerical examples with simulations are presented to demonstrate the effectiveness and merits of the proposed methods

multistability of almost periodic solution for memristive cohen grossberg neural networks with mixed delays this paper presents the multistability analysis of almost periodic state solutions for memristive cohen grossberg neural networks mcgnns with both distributed delay and discrete delay the activation function of the considered mcgnns is generalized to be nonmonotonic and nonpiecewise linear it is shown that the mcgnns with n neuron have k 1 n locally exponentially stable almost periodic solutions where nature number k depends on the geometrical structure of the considered activation function compared with the previous related works the number of almost periodic state solutions of the mcgnns is extensively increased the obtained conclusions in this paper are also capable of studying the multistability of equilibrium points or periodic solutions of the mcgnns moreover the enlarged attraction basins of attractors are estimated based on original partition some comparisons and convincing numerical examples are provided to substantiate the superiority and efficiency of obtained results

global stability analysis in cohen grossberg neural networks with delays and inverse holder neuron activation functions in this paper a novel class of cohen grossberg neural networks with delays and inverse holder neuron activation functions are presented by using the topological degree theory and linear matrix inequality lmi technique the existence and uniqueness of equilibrium point for such cohen grossberg neural networks is investigated by constructing appropriate lyapunov function a sufficient condition which ensures the global exponential stability of the equilibrium point is established two numerical examples are provided to demonstrate the effectiveness of the theoretical results

multistability analysis for a general class of delayed cohen grossberg neural networks in this paper by discussing parameter conditions based on properties of activation functions we decompose state space into positively invariant sets and establish sufficient conditions for the existence of locally stable equilibria for delayed cohen grossberg neural networks cgnns through cauchy convergence principle some new criteria are derived for ensuring equilibria periodic orbits to be locally or globally exponentially stable in any designated region finally our results are demonstrated by four numerical simulations

2n almost periodic attractors for cohen grossberg type bam neural networks with variable coefficients and distributed delays abstract in this paper a class of 2 n almost periodic attractors for cohen grossberg type bi directional associative memory bam neural networks with variable coefficients and distributed delays is discussed by imposing some new assumptions on activation functions and system parameters we split invariant basin of bam into 2 n compact convex subsets then the existence of 2 n almost periodic solutions lying in compact convex subsets is attained and some new criteria for the networks to converge toward these 2 n almost periodic solutions and exponential attracting domains are also given correspondingly finally some examples are presented to illustrate the feasibility and effectiveness of the results

existence and global exponential stability of almost periodic solution for cellular neural networks with variable coefficients and time varying delays in this paper we study cellular neural networks with almost periodic variable coefficients and time varying delays by using the existence theorem of almost periodic solution for general functional differential equations introducing many real parameters and applying the lyapunov functional method and the technique of young inequality we obtain some sufficient conditions to ensure the existence uniqueness and global exponential stability of almost periodic solution the results obtained in this paper are new useful and extend and improve the existing ones in previous literature

sampled data fuzzy control of chaotic systems based on a t s fuzzy model in this paper a sampled data fuzzy controller is designed to stabilize a class of chaotic systems a takagi sugeno t s fuzzy model is employed to represent the chaotic systems based on this general model the exponential stability issue of the closed loop systems with an input constraint is first investigated by a novel time dependent lyapunov functional which is positive definite at sampling times but not necessary between the sampling times then two sufficient conditions are developed for sampled data fuzzy controller synthesis of the underlying t s fuzzy model with or without input constraint all the proposed results in this paper depend on both the upper and lower bounds on a sampling interval and the available information about the actual sampling pattern is fully utilized the proposed sampled data fuzzy control scheme is successfully applied to the chaotic lorenz system which is shown to be effective and less conservative compared with existing results

network based h_ infty filtering for discrete time systems this correspondence is concerned with network based h filtering for discrete time systems the output signals of the system under consideration are transmitted to the filter through a constraint communication network which usually leads to network induced delays and packet dropouts by introducing a logic data packet processor to choose the newest data signal from the network to actuate the filter network induced delays and packet dropouts are modeled as a markov chain taking values in a finite set as a result the filter to be designed is modeled as a markov jumping linear filter by introducing some slack matrix variables in terms of prob ability identity a less conservative bounded real lemma brl is derived to ensure that the filtering error system is stochastically stable with a pre scribed h level based on this brl suitable h filters are designed by employing a cone complementary approach a practical example on the leslie model about some certain pest s structured population dynamics is given to show the effectiveness of the proposed approach

periodic solutions for semi linear evolution inclusions in this paper we study the periodic problem for semi linear evolution inclusion using techniques from multivalued analysis and fixed point theorems we establish existence theorems under the one sided lipschitz condition first we prove existence theorems for nonconvex and convex problem second we look for extremal periodic solutions and prove the relaxation theorem

global exponential stability and global convergence in finite time of delayed neural networks with infinite gain this paper introduces a general class of neural networks with arbitrary constant delays in the neuron interconnections and neuron activations belonging to the set of discontinuous monotone increasing and possibly unbounded functions the discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high and tends to infinity while the delay accounts for the finite switching speed of the neuron amplifiers or the finite signal propagation speed it is known that the delay in combination with high gain nonlinearities is a particularly harmful source of potential instability the goal of this paper is to single out a subclass of the considered discontinuous neural networks for which stability is instead insensitive to the presence of a delay more precisely conditions are given under which there is a unique equilibrium point of the neural network which is globally exponentially stable for the states with a known convergence rate the conditions are easily testable and independent of the delay moreover global convergence in finite time of the state and output is investigated in doing so new interesting dynamical phenomena are highlighted with respect to the case without delay which make the study of convergence in finite time significantly more difficult the obtained results extend previous work on global stability of delayed neural networks with lipschitz continuous neuron activations and neural networks with discontinuous neuron activations but without delays

passive learning and input to state stability of switched hopfield neural networks with time delay in this paper we propose a new passive weight learning law for switched hopfield neural networks with time delay under parametric uncertainty based on the proposed passive learning law some new stability results such as asymptotical stability input to state stability iss and bounded input bounded output bibo stability are presented an existence condition for the passive weight learning law of switched hopfield neural networks is expressed in terms of strict linear matrix inequality lmi finally numerical examples are provided to illustrate our results

filter design for interval type 2 fuzzy systems with d stability constraints under a unified frame this paper investigates the problem of filter design for interval type 2 it2 fuzzy systems with d stability constraints based on a new performance index attention is focused on solving the h infty l 2 l infty passive and dissipativity fuzzy filter design problems for it2 fuzzy systems with d stability constraints in a unified frame under the new performance index frame using lyapunov stability theory a novel type of it2 filter is designed such that the filtering error system guarantees the prescribed h infty l 2 l infty passive and dissipativity performance levels with d stability constraints the existence condition of the it2 filter is expressed as the convex optimization problem and the filter parameters in the condition can be solved by the standard software the it2 fuzzy model and it2 fuzzy filter do not need to share the same lower and upper membership functions finally a numerical example is provided to show the effectiveness of the proposed results

global stability analysis of a general class of discontinuous neural networks with linear growth activation functions this paper investigates the global asymptotic stability of the periodic solution for a general class of neural networks whose neuron activation functions are modeled by discontinuous functions with linear growth property by using leray schauder alternative theorem the existence of the periodic solution is proved based on the matrix theory and generalized lyapunov approach a sufficient condition which ensures the global asymptotical stability of a unique periodic solution is presented the obtained results can be applied to check the global asymptotical stability of discontinuous neural networks with a broad range of activation functions assuming neither boundedness nor monotonicity and also conform the validity of forti s conjecture for discontinuous neural networks with linear growth activation functions two illustrative examples are given to demonstrate the effectiveness of the present results

global nonfragile synchronization in finite time for fractional order discontinuous neural networks with nonlinear growth activations this paper is concerned with the global nonfragile mittag leffler synchronization and the global synchronization in finite time for fractional order discontinuous neural networks where activation functions are discontinuous at 0 or modeled as a local holder functions with the nonlinear growth property in a neighborhood of 0 first two lemmas concerned with the convergence with respect to an absolutely continuous function are developed second a new property which introduces an inequality of the fractional derivative for the variable upper limit integral with respect to the nonsmooth integrable function is presented and applied in the synchronization results analysis in addition under the fractional filippov differential inclusion framework by utilizing the lur e postnikov type lyapunov functional nonsmooth analysis method and the convergence properties developed in this paper the synchronization conditions are derived in the form of linear matrix inequalities moreover the upper bound of the setting time for the global nonfragile synchronization in finite time is calculated accurately finally two illustrations are presented to verify the correctness of the theoretical results

existence and global exponential stability of periodic solution of cellular neural networks with time varying delays by using the continuation theorem of mawhin s coincidence degree theory and gronwall s inequality some new sufficient conditions are obtained ensuring existence and global exponential stability of periodic solution of cellular neural networks with periodic coefficients and delays these results are helpful to design globally exponentially stable and oscillatory cellular neural networks

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

dynamical analysis of neural networks of subgradient system in this technical note we consider a class of neural network which is a generalization of neural network models considered in the optimization context under some mild assumptions this neural network can be translated into a negative subgradient dynamical system at first we study the existence and uniqueness of solution of this neural network then by nonsmooth ojasiewicz inequality we prove the convergence of the trajectories of this neural network in the end a constrained minimization problem is studied which can be associated with this neural network it is proved that the local constrained minimum of the cost function coincides with the stable equilibria point of this neural network

fuzzy differential equations abstract this paper deals with fuzzy set valued mappings of a real variable whose values are normal convex upper semicontinuous and compactly supported fuzzy sets in rn we study differentiability and integrability properties of such functions and give an existence and uniqueness theorem for a solution to a fuzzy differential equation

lmi optimization approach on stability for delayed neural networks of neutral type in this paper the global asymptotic stability of delayed cellular neural networks of neutral type is investigated a novel delay dependent criterion for the stability using the lyapunov stability theory and linear matrix inequality lmi framework is presented since the condition is dependent on the size of time delay it is usually less conservative than delay independent ones two numerical examples are given to show the effectiveness of proposed method

a new method for stabilization of networked control systems with random delays we consider the stabilization problem for a class of networked control systems in the discrete time domain with random delays the sensor to controller and controller to actuator delays are modeled as two markov chains and the resulting closed loop systems are jump linear systems with two modes the necessary and sufficient conditions on the existence of stabilizing controllers are established it is shown that state feedback gains are mode dependent an iterative linear matrix inequality lmi approach is employed to calculate the state feedback gains

event based h filtering for sampled data systems this paper is concerned with event based h filtering for sampled data systems first an event based data packet processor is introduced to release sampled measurement outputs only if an event condition is violated as a result communication resources can be saved significantly while preserving the desired h performance second the resulting filtering error system is modeled as a system with an interval time varying delay by employing the lyapunov krasovskii functional approach a new bounded real lemma brl is established such that the filtering error system is asymptotically stable with the prescribed h performance third by performing an invertible linear transformation on the filtering error system a linear matrix inequality lmi based sufficient condition which is equivalent to the condition in the brl is obtained on the feasibility of the event based h filtering problem consequently suitable h filters and the event parameters in the event condition can be co designed provided that a set of lmis are satisfied finally a mechanical system with two masses and two springs is given to show the effectiveness of the proposed method

exponential stability on stochastic neural networks with discrete interval and distributed delays this brief addresses the stability analysis problem for stochastic neural networks snns with discrete interval and distributed time varying delays the interval time varying delay is assumed to satisfy 0 d1 d t d2 and is described as d t d 1 h t with 0 h t d 2 d 1 based on the idea of partitioning the lower bound d 1 new delay dependent stability criteria are presented by constructing a novel lyapunov krasovskii functional which can guarantee the new stability conditions to be less conservative than those in the literature the obtained results are formulated in the form of linear matrix inequalities lmis numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results

pattern selectivity in neural networks as a means of understanding basin structures the authors study the problem of learning and retrieving for a pair of correlated patterns within an extensive number of uncorrelated patterns for networks where learning may be treated as an optimization process with respect to an arbitrary cost function this formulation is then applied to several specific examples where they study the pattern selectivity of these systems i e their ability to differentiate correlated patterns and investigate the process of basin shrinking with increasing loading levels they define and discuss several different retrieval phases whose existence depends on the competitive interplay of the loading level and the pattern correlation discussion of asymptotic retrieval is restricted to dilute asymmetric networks

generalized lyapunov approach for convergence of neural networks with discontinuous or non lipschitz activations the paper considers a class of additive neural networks where the neuron activations are modeled by discontinuous functions or by continuous non lipschitz functions some tools are developed which enable us to apply a lyapunov like approach to differential equations with discontinuous right hand side modeling the neural network dynamics the tools include a chain rule for computing the time derivative along the neural network solutions of a nondifferentiable lyapunov function and a comparison principle for this time derivative which yields conditions for exponential convergence or convergence in finite time by means of the lyapunov like approach a general result is proved on global exponential convergence toward a unique equilibrium point of the neural network solutions moreover new results on global convergence in finite time are established which are applicable to neuron activations with jump discontinuities or neuron activations modeled by means of continuous non lipschitz holder functions

h sub spl infin control for networked systems with random communication delays this note is concerned with a new controller design problem for networked systems with random communication delays two kinds of random delays are simultaneously considered i from the controller to the plant and ii from the sensor to the controller via a limited bandwidth communication channel the random delays are modeled as a linear function of the stochastic variable satisfying bernoulli random binary distribution the observer based controller is designed to exponentially stabilize the networked system in the sense of mean square and also achieve the prescribed h sub spl infin disturbance attenuation level the addressed controller design problem is transformed to an auxiliary convex optimization problem which can be solved by a linear matrix inequality lmi approach an illustrative example is provided to show the applicability of the proposed method

a delay dependent approach to robust h sub spl infin filtering for uncertain discrete time state delayed systems a delay dependent approach to robust h sub spl infin filtering is proposed for linear discrete time uncertain systems with multiple delays in the state the uncertain parameters are supposed to reside in a polytope and the attention is focused on the design of robust filters guaranteeing a prescribed h sub spl infin noise attenuation level the proposed filter design methodology incorporates some recently appeared results such as moon s new version of the upper bound for the inner product of two vectors and de oliveira s idea of parameter dependent stability which greatly reduce the overdesign introduced in the derivation process in addition to the full order filtering problem the challenging reduced order case is also addressed by using different linearization procedures both full and reduced order filters can be obtained from the solution of convex optimization problems in terms of linear matrix inequalities which can be solved via efficient interior point algorithms numerical examples have been presented to illustrate the feasibility and advantages of the proposed methodologies

robust synchronisation control of discontinuous cgnns with time varying delays fixed time synchronisation requires that the settling time is independent of the initial synchronisation errors and the upper bound of the settling time can be given in advance from this point of

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

common asymptotic behavior of solutions and almost periodicity for discontinuous delayed and impulsive neural networks the paper considers a general neural network model with impulses at a given sequence of instants discontinuous neuron activations delays and time varying data and inputs it is shown that when the neuron interconnections satisfy an m matrix condition or a dominance condition then the state solutions and the output solutions display a common asymptotic behavior as time t it is also shown via a new technique based on prolonging the solutions of the delayed neural network to that it is possible to select a unique special solution that is globally exponentially stable and can be considered as the unique global attractor for the network finally this paper shows that for almost periodic data and inputs the selected solution is almost periodic moreover it is robust with respect to a large class of perturbations of the data analogous results also hold for periodic data and inputs a by product of the analysis is that a sequence of almost periodic impulses is able to induce in the generic case nonstationary almost periodic solutions in an otherwise globally convergent nonimpulsive neural network to the authors knowledge the results in this paper are the only available results on global exponential stability of the unique periodic or almost periodic solution for a general neural network model combining three main features i e impulses discontinuous neuron activations and delays the results in this paper are compared with several results in the literature dealing with periodicity or almost periodicity of some subclasses of the neural network model here considered and some hints for future work are given

storing correlated patterns in hopfield s model the authors study the retrieval properties of hopfield s model 1982 when there is a finite correlation between two of the memorised patterns

dissipativity and synchronization of generalized bam neural networks with multivariate discontinuous activations this paper is concerned with the dissipativity and synchronization problems of a class of delayed bidirectional associative memory bam neural networks in which neuron activations are modeled by discontinuous bivariate functions first the concept of the filippov solution is extended to functional differential equations with discontinuous right hand sides and mixed delays via functional differential inclusions the global dissipativity of the filippov solution to the considered bam neural networks is proven using generalized halanay inequalities and matrix measure approaches second to realize global exponential complete synchronization of bam neural networks with multivariate discontinuous activations discontinuous state feedback controllers are designed using functional differential inclusions theory and nonsmooth analysis theory with generalized lyapunov functional method finally several numerical examples are provided to demonstrate the applicability and effectiveness of our proposed results

non affine nonlinear adaptive control of decentralized large scale systems using neural networks this paper introduces a new decentralized adaptive neural network controller for a class of large scale nonlinear systems with unknown non affine subsystems and unknown interconnections represented by nonlinear functions a radial basis function neural network is used to represent the controller s structure the stability of the closed loop system is guaranteed through lyapunov stability analysis the effectiveness of the proposed decentralized adaptive controller is illustrated by considering two nonlinear systems a two inverted pendulum and a turbo generator the simulation results verify the merits of the proposed controller

dynamical behaviors of delayed neural network systems with discontinuous activation functions in this letter without assuming the boundedness of the activation functions we discuss the dynamics of a class of delayed neural networks with discontinuous activation functions a relaxed set of sufficient conditions is derived guaranteeing the existence uniqueness and global stability of the equilibrium point convergence behaviors for both state and output are discussed the constraints imposed on the feedback matrix are independent of the delay parameter and can be validated by the linear matrix inequality technique we also prove that the solution of delayed neural networks with discontinuous activation functions can be regarded as a limit of the solutions of delayed neural networks with high slope continuous activation functions

l1 gain performance analysis and positive filter design for positive discrete time markov jump linear systems a linear programming approach abstract this paper is concerned with the l 1 gain performance analysis and positive filter design for a positive discrete time markov jump linear system mjls by using a linear programming lp approach first by constructing a linear stochastic lyapunov function and introducing an equivalent deterministic positive discrete time linear system necessary and sufficient conditions in the form of lp are derived for stochastic stability and l 1 gain performance of the positive discrete time mjls then a sufficient condition on the existence of the desired positive l 1 gain filter is provided the desired positive l 1 gain filter can be designed by solving a standard lp problem the pest s structured population dynamic model is employed to illustrate the effectiveness of the proposed method

almost periodic solutions for cellular neural networks with time varying delays in leakage terms this paper concerns with cellular neural networks with time varying delays in leakage or forgetting terms without assuming boundedness on activation functions we establish sufficient conditions on existence and global exponential stability of almost periodic solutions by using lyapunov functional method and differential inequality techniques our results complement with some recent ones

stability analysis of a delayed hopfield neural network in this paper we study a class of neural networks which includes bidirectional associative memory networks and cellular neural networks as its special cases by brouwer s fixed point theorem a continuation theorem based on gains and mawhin s coincidence degree matrix theory and inequality analysis we not only obtain some different sufficient conditions ensuring the existence uniqueness and global exponential stability of the equilibrium but also estimate the exponentially convergent rate our results are less restrictive than previously known criteria and can be applied to neural networks with a broad range of activation functions assuming neither differentiability nor strict monotonicity

on stability criteria for uncertain delay differential systems of neutral type with time varying delays in this paper we propose a new stability criterion for uncertain neutral systems the considered system has time varying structured uncertainties and time varying delay based on the lyapunov method a delay dependent criterion for asymptotic stability is derived in terms of lmi linear matrix inequality numerical examples are given to show the effectiveness of our results