a new stability analysis of delayed cellular neural networks in this paper the global asymptotic stability of delayed cellular neural networks dcnns is investigated a novel criterion for the stability using the lyapunov stability theory and linear matrix inequality lmi framework is presented the criterion expressed by lmis is delay dependent the result is less conservative than those established in the earlier references a numerical example is given to show the effectiveness of proposed method

design of state estimator for neural networks of neutral type in this paper the design problem of state estimator for a class of neural networks of neutral type is studied a delay dependent linear matrix inequality lmi criterion for existence of the estimator is proposed by using the lyapunov method the criterion can be easily solved by various convex optimization algorithms a numerical example with simulation results is given to show the effectiveness of proposed method

global exponential stability of cellular neural networks with variable delays for cellular neural networks with time varying delays the problems of determining the exponential stability and estimating the exponential convergence rate are investigated by employing the lyapunov krasovskii functional and linear matrix inequality lmi technique a novel criterion for the stability which give information on the delay dependent property is derived two examples are given to demonstrate the effectiveness of the obtained results

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

lmi based criteria for global robust stability of bidirectional associative memory networks with time delay abstract in this paper several new sufficient conditions are given to ensure existence uniqueness and globally exponential robust stability of the equilibrium point for bidirectional associative memory bam networks with delays this novel approach based on the linear matrix inequality lmi technique removes some existing restrictions on the system s parameters and the derived conditions are easy to verify via the lmi toolbox in addition two examples are given to show the effectiveness and the advantage of the proposed results

multistability and multiperiodicity of delayed bidirectional associative memory neural networks with discontinuous activation functions abstract in this paper the multistability and multiperiodicity issues are discussed for 2 n dimensional delayed bidirectional associative memory neural networks with r level discontinuous activation functions sufficient conditions are established to ensure the existence of r n locally exponentially stable equilibria as an extension of multistability conditions are established to ensure the existence of r n locally exponentially stable periodic solutions when external inputs are periodic finally two examples are given to illustrate the effectiveness of the obtained results

new exponential stability results for delayed neural networks with time varying delays abstract this paper presents new sufficient conditions for the uniqueness and exponential stability of the equilibrium point for delayed neural networks with time varying delays by employing a more general type of lyapunov krasovskii functional and using lmi linear matrix inequality we derive new results for exponential stability of the equilibrium point for delayed neural networks the results establish a relation between the delay time and the parameters of the network the results are compared with the previous results derived in the literature

hybrid driven based h filter design for neural networks subject to deception attacks abstract this paper investigates the problem of h filter design for neural networks with hybrid triggered scheme and deception attacks in order to make full use of the limited network resources a hybrid triggered scheme is introduced in which the switching between the time triggered scheme and the event triggered scheme obeys bernoulli distribution by considering the effect of hybrid triggered scheme and deception attacks a mathematical model of h filtering error system is constructed the sufficient conditions that can ensure the stability of filtering error system are given by using lyapunov stability theory and linear matrix inequality lmi techniques moreover the explicit expressions are provided for the designed filter parameters that is in terms of lmis finally a numerical example is employed to illustrate the design method

existence and stability of almost periodic solution for bam neural networks with delays by using the banach fixed point theorem and constructing suitable lyapunov function some sufficient conditions are obtained ensuring existence uniqueness and global stability of almost periodic solution of the bam neural networks with variable coefficients and delays these results are helpful to design global exponential stable bam networks and almost periodic oscillatory bam networks

design of state estimator for bam fuzzy cellular neural networks with leakage and unbounded distributed delays in this paper the state estimation problem is studied for the bidirectional associative memory bam fuzzy cellular neural networks fcnns with delays where the delays include leakage and unbounded distributed delays the problem addressed is to estimate the neuron states through available output measurements such that for all admissible leakage and unbounded distributed delays the dynamics of the estimation error is globally asymptotically stable a delay dependent linear matrix inequality lmi criterion for the existence of the estimator is proposed by using the lyapunovkrasovskii functional method in addition the unknown gain matrix is determined by solving a delay dependent lmi finally numerical examples and simulations are provided to illustrate the effectiveness of the theoretical results

on poincare type inequalities using estimates of the heat kernel we prove a poincare inequality for star shape domains on a complete manifold the method also gives a lower bound for the gap of the first two neumann eigenvalues of a schrodinger operator

new robust stability results for bidirectional associative memory neural networks with multiple time delays in this paper the robust stability problem is investigated for a class of bidirectional associative memory bam neural networks with multiple time delays by employing suitable lyapunov functionals and using the upper bound norm for the interconnection matrices of the neural network system some novel sufficient conditions ensuring the existence uniqueness and global robust stability of the equilibrium point are derived the obtained results impose constraint conditions on the system parameters of neural network independent of the delay parameters some numerical examples and simulation results are given to demonstrate the applicability and effectiveness of our results and to compare the results with previous robust stability results derived in the literature

new results for global stability of a class of neutral type neural systems with time delays this paper studies the global convergence properties of a class of neutral type neural networks with discrete time delays this class of neutral systems includes cohen grossberg neural networks hopfield neural networks and cellular neural networks based on the lyapunov stability theorems some delay independent sufficient conditions for the global asymptotic stability of the equilibrium point for this class of neutral type systems are derived it is shown that the results presented in this paper for neutral type delayed neural networks are the generalization of a recently reported stability result a numerical example is also given to demonstrate the applicability of our proposed stability criteria

improved global robust stability of interval delayed neural networks via split interval generalizations abstract the problem of global robust stability of hopfield type delayed neural networks with the intervalized network parameters is revisited recently a computationally tractable i e linear matrix inequality lmi based global robust stability criterion derived from an earlier criterion based on dividing the given interval into more that two intervals has been presented in the present paper generalizations i e division of the given interval into m intervals where m is an integer greater than or equal to 2 is considered and some new lmi based global robust stability criteria are derived it is shown that in some cases m 2 may not suffice i e m 2 may be needed to realize the improvement an example showing the effectiveness of the proposed generalization is given the paper also provides a complete and systematic explanation of the split interval idea

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

new results on stability analysis for delayed markovian generalized neural networks with partly unknown transition rates the stability of delayed markovian generalized neural networks is studied where the transition rates of the modes are partly unknown the partly unknown transition rates generalize the traditional works that are with all known transition rates then a lyapunov krasovskii functional lkf with a delay product type dpt term is constructed the dpt term is not only simple but also fully utilizes the information of time delay based on the new dpt lkf stability criteria are presented which are with lower computational complexity and less conservative in the end the validity and superiorities of the analytical results are verified by several examples

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

criticality of the potts ferromagnet in migdal kadanoff like hierarchical lattices within the real space renormalisation group framework the authors discuss the critical point and exponent nu of the potts ferromagnet in b sized migdal kadanoff like hierarchical lattices both b to infinity and b to 1 limits are exhibited the important discrepancies that might exist between the exact results for d dimensional hierarchical lattices and d dimensional bravais lattices are illustrated

a new stability criterion for bidirectional associative memory neural networks of neutral type in the paper the global asymptotic stability of equilibrium is considered for continuous bidirectional associative memory bam neural networks of neutral type by using the lyapunov method a new stability criterion is derived in terms of linear matrix inequality lmi to ascertain the global asymptotic stability of the bam the lmi can be solved easily by various convex optimization algorithms a numerical example is illustrated to verify our result

robust stability of uncertain fuzzy bam neural networks of neutral type with markovian jumping parameters and impulses abstract in this paper the problem of neutral type impulsive bidirectional associative memory neural networks nibamnns with time delays are first established by a takagi sugeno t s fuzzy model in which the consequent parts are composed of a set of nibamnns with interval delays and markovian jumping parameters mjps sufficient conditions to check the robust exponential stability of the derived model are based on the lyapunov krasovskii functionals lkfs containing some novel triple integral terms lyapunov stability theory and employing the free weighting matrix method the delay dependent stability conditions are established in terms of linear matrix inequalities lmis which can be very efficiently solved using matlab lmi control toolbox finally numerical examples and remarks are given to illustrate the effectiveness and usefulness of the derived results

a generalized lmi based approach to the global asymptotic stability of delayed cellular neural networks a novel linear matrix inequality lmi based criterion for the global asymptotic stability and uniqueness of the equilibrium point of a class of delayed cellular neural networks cnns is presented the criterion turns out to be a generalization and improvement over some previous criteria

design of state estimator for neural networks with leakage discrete and distributed delays abstract in this paper the design problem of state estimator for neural networks with mixed time varying delays and leakage delays has been investigated by using appropriate model transformation that shifts the considered systems into the neutral type time delay systems adapting a new lyapunov krasovskii functional which takes into account the range of time delay and by making use of some inequality techniques delay dependent criteria are developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable the criteria are formulated in terms of a set of linear matrix inequalities lmis which can be checked efficiently by use of some standard numerical packages finally three numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the presented results

lmi conditions for global asymptotic stability results for neutral type neural networks with distributed time delays abstract in this paper the global asymptotic stability of neutral type neural networks with unbounded distributed delays is analyzed by utilizing the lyapunov krasovskii functional and the linear matrix inequality lmi approach a new sufficient condition ensuring the global asymptotic stability for neutral type neural networks is obtained by using the powerful matlab lmi toolbox two examples are provided to illustrate the applicability of the stability results

existence of an exponential periodic attractor of periodic solutions for general bam neural networks with time varying delays and impulses abstract by applying inequality analysis technique constructing a suitable lyapunov functional and combing the continuation theorem due to mawhin and gaines some novel sufficient conditions are obtained to ensure the existence of an exponential periodic attractor of periodic solutions for general neural networks with time varying including bounded and unbounded delays and impulses an example is employed to illustrate our feasible results

an analysis of global asymptotic stability of delayed cellular neural networks in this paper a new sufficient condition is given for the uniqueness and global asymptotic stability of the equilibrium point for delayed cellular neural networks dcnns this condition imposes constraints on the feedback and delayed feedback matrices of a dcnn independently of the delay parameter this result is also compared with the previous results derived in the literature

delay fractioning approach to robust exponential stability of fuzzy cohen grossberg neural networks abstract in this paper the problem of robust exponential stability analysis for a class of takagi sugeno ts fuzzy cohen grossberg neural networks with uncertainties and time varying delays is investigated a generalized activation function is used and the assumptions such as boundedness monotony and differentiability of the activation functions are removed by using a lyapunov krasovskii functional and employing the delay fractioning approach a set of sufficient conditions are established for achieving the required result the obtained conditions are proposed in terms of linear matrix inequalities lmis so its feasibility can be checked easily via standard numerical toolboxs the main advantage of the proposed criteria lies in its reduced conservatism which is mainly based on the time delay fractioning technique in addition to that a numerical example with simulation results is given to show the effectiveness of the obtained lmi conditions

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