energy to peak state estimation for markov jump rnns with time varying delays via nonsynchronous filter with nonstationary mode transitions in this paper the problem of energy to peak state estimation for a class of discrete time markov jump recurrent neural networks rnns with randomly occurring nonlinearities rons and time varying delays is investigated a practical phenomenon of nonsynchronous jumps between rnns modes and desired mode dependent filters is considered and a nonstationary mode transition among the filters is used to model the nonsynchronous jumps to different degrees that are also mode dependent the rons are used to model a class of sector like nonlinearities that occur in a probabilistic way according to a bernoulli sequence the time varying delays are supposed to be mode dependent and unknown but with known lower and upper bounds a priori sufficient conditions on the existence of the nonsynchronous filters are obtained such that the filtering error system is stochastically stable and achieves a prescribed energy to peak performance index further to the recent study on the class of nonsynchronous estimation problem a monotonicity is observed in obtaining filtering performance index while changing the degree of nonsynchronous jumps a numerical example is presented to verify the theoretical findings

stability analysis for neural networks with time varying interval delay this letter is concerned with the stability analysis of neural networks nns with time varying interval delay the relationship between the time varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the derivative of lyapunov functional as a result some improved delay interval dependent stability criteria for nns with time varying interval delay are proposed numerical examples are given to demonstrate the effectiveness and the merits of the proposed method

model approximation for switched genetic regulatory networks the model approximation problem is studied in this paper for switched genetic regulatory networks grns with time varying delays we focus on constructing a reduced order model to approximate the high order grns considered under the switching signal subject to certain constraints such that the approximation error system between the original and reduced order systems is exponentially stable with a disturbance attenuation performance the stability conditions and the disturbance attenuation performance are established by utilizing two integral inequality bounding techniques and the average dwell time method for the approximation error system then the solvability conditions for the reduced order models for the grns are also established using the projection method furthermore the model approximation problem can be transferred into a sequential minimization problem that is subject to linear matrix inequality constraints by using the cone complementarity algorithm finally several examples are provided to illustrate the effectiveness and the advantages of the proposed methods

stability and synchronization of discrete time neural networks with switching parameters and time varying delays this paper is concerned with the problems of exponential stability analysis and synchronization of discrete time switched delayed neural networks using the average dwell time approach together with the piecewise lyapunov function technique sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with time delays benefitting from the delay partitioning method and the free weighting matrix technique the conservatism of the obtained results is reduced in addition the decay estimates are explicitly given and the synchronization problem is solved the results reported in this paper not only depend upon the delay but also depend upon the partitioning which aims at reducing the conservatism numerical examples are presented to demonstrate the usefulness of the derived theoretical results

exponential stability analysis for delayed neural networks with switching parameters average dwell time approach this paper is concerned with the problem of exponential stability analysis of continuous time switched delayed neural networks by using the average dwell time approach together with the piecewise lyapunov function technique and by combining a novel lyapunov krasovskii functional which benefits from the delay partitioning method with the free weighting matrix technique sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with constant and time varying delays respectively moreover the decay estimates are explicitly given the results reported in this paper not only depend upon the delay but also depend upon the partitioning which aims at reducing the conservatism numerical examples are presented to demonstrate the usefulness of the derived theoretical results

traces of fuzzy binary relations abstract in this paper the right and left trace of a fuzzy binary relation is defined and investigated basic properties of a fuzzy relation such as reflexivity different kinds of transitivity linearity etc are characterized by means of its traces

estimation of domain of attraction for aperiodic sampled data switched delayed neural networks subject to actuator saturation in this paper for the case of the asynchronous switching caused by that subsystem s switching occuring during a sampling interval the domain of attraction estimation problem is investigated for aperiodic sampled data switched delayed neural networks asdsdnns subject to actuator saturation a parameters dependent time scheduled lyapunov functional consisting of a novel looped functional is constructed using segmentation technology and linear interpolation by employing this novel functional and using an average dwell time adt approach exponential stability criteria are proposed for polytopic uncertain asdsdnns subject to actuator saturation and a relationship between adt and sampling period is revealed for asdsdnns as a corollary exponential stability criteria are proposed for nominal asdsdnns subject to actuator saturation furthermore by describing the domain of attraction as a time varying ellipsoid determined by the time scheduled lyapunov matrix the proposed theoretical conditions are transformed into a linear matrix inequality lmi based multi objective optimization problem the dynamic estimates of the domain of attraction for asdsdnns are solved numerical simulation examples are provided to illustrate the effectiveness of the proposed method

a note on the q ary image of a q sup m ary repeated root cyclic code for n q p sup s where p ch f sub q s spl ges 1 v a q sup m ary repeated root cyclic code of length n with generator polynomial g x we give a partial answer about whether the q ary image of v is cyclic or not with respect to a certain basis for f sub q m over f sub q

passivity of delayed reaction diffusion networks with application to a food web model this paper is concerned with the passivity problem of a class of delayed reaction diffusion networks in which the input and output variables are varied with the time and space variables firstly two new passivity definitions are proposed which are totally different from some existing concepts of passivity then we establish several criteria for the passivity input strict passivity and output strict passivity by using the lyapunov functional method combined with the inequality techniques and also reveal the relationship between passivity and stability of the reaction diffusion networks moreover these results are applied to investigate the asymptotic stability and control design of a food web model with time delay and reaction diffusion terms numerical simulations are presented finally to demonstrate the effectiveness of the proposed results

further result on guaranteed h_ infty performance state estimation of delayed static neural networks this brief considers the guaranteed h infty performance state estimation problem of delayed static neural networks an arcak type state estimator which is more general than the widely adopted luenberger type one is chosen to tackle this issue a delay dependent criterion is derived under which the estimation error system is globally asymptotically stable with a prescribed h infty performance it is shown that the design of suitable gain matrices and the optimal performance index are accomplished by solving a convex optimization problem subject to two linear matrix inequalities compared with some previous results much better performance is achieved by our approach which is greatly benefited from introducing an additional gain matrix in the domain of activation function an example is finally given to demonstrate the advantage of the developed result

dissipative reliable controller design for uncertain systems and its application this paper address the reliable robust strictly q r s dissipative control problem for a class of uncertain continuous time systems with input delay and linear fractional uncertainties despite possible actuator failures in system model in particular by employing a novel lyapunov functional together with delay fractioning approach a reliable control law is designed in terms of the solution of certain linear matrix inequalities which makes the considered system strictly q r s dissipative which can be easily solved by using the available software at the same time as special cases the h passivity mixed h and passivity control problems can obtained from the proposed dissipative control formulation this explains the fact that the dissipative control unifies h control passive control and mixed h and passivity control in a single framework finally a numerical example based on a mechanical system is given to demonstrate the effectiveness and applicability of the proposed design approach more precisely the proposed results have been compared through numerical simulation which reveals that the obtained criteria are considerably less conservative than some existing results

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

delay dependent state estimation for delayed neural networks in this letter the delay dependent state estimation problem for neural networks with time varying delay is investigated a delay dependent criterion is established to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable the proposed method is based on the free weighting matrix approach and is applicable to the case that the derivative of a time varying delay takes any value an algorithm is presented to compute the state estimator finally a numerical example is given to demonstrate the effectiveness of this approach and the improvement over existing ones

boundedness and stability for cohen grossberg neural network with time varying delays abstract in this paper a model is considered to describe the dynamics of cohen grossberg neural network with variable coefficients and time varying delays uniformly ultimate boundedness and uniform boundedness are studied for the model by utilizing the hardy inequality combining with the halanay inequality and the lyapunov functional method some new sufficient conditions are derived for the model to be globally exponentially stable the activation functions are not assumed to be differentiable or strictly increasing moreover no assumption on the symmetry of the connection matrices is necessary these criteria are important in signal processing and the design of networks

improved delay dependent stability condition of discrete recurrent neural networks with time varying delays this brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time varying delays in terms of linear matrix inequality lmi approach a novel delay dependent stability criterion is established for the considered recurrent neural networks via a new lyapunov function the obtained condition has less conservativeness and less number of variables than the existing ones numerical example is given to demonstrate the effectiveness of the proposed method

on fuzzy strict preference indifference and incomparability relations abstract the existence of fuzzy models for strict preference indifference and incomparability satisfying all classical properties is established a particular class of such models is completely described

distributed non fragile filtering in sensor networks with energy constraints this paper investigates the distributed filtering problem for sensor networks with energy constraint and filter gain variation firstly two strategies are proposed to reduce energy consumption i e the measurement size and the transmission rate reduction strategies to reduce the measurement size a useful measurement selection policy is introduced to choose which element of local measurements is transmitted the selected measurement which has a smaller size than the original one is then stochastically transmitted in order to reflect the imprecision in filter implementation the additive filter gain variation problem is considered based on the switched system approach and some stochastic system analysis methods a sufficient condition is obtained such that the filtering error system is exponentially stable in the mean square sense and a prescribed h performance level is guaranteed filter gains are shown to be determined by solving a set of linear matrix inequalities lmis finally a simulation example is given to demonstrate the effectiveness of the proposed design

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

simultaneous fault detection and control for switched linear systems with mode dependent average dwell time this paper investigates the problem of the simultaneous fault detection and control sfdc for switched linear systems to meet the control and detection objectives the time dependent detection filters and dynamic output feedback controllers are presented in sfdc under a mixed h h framework a mode dependent average dwell time mdadt approach which means that each subsystem has its own average dwell time is adopted in this paper to reduce the conservativeness of the average dwell time method and the discretized lyapunov function dlf technique is first used to relax the mdadt constraints in sfdc some sufficient conditions for designing filters controllers which satisfy the h h performance are given in terms of linear matrix inequalities lmis what s more a two step algorithm to solve the sfdc problem is proposed the effectiveness of the proposed method is illustrated through two simulation examples

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

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

bogdanov takens singularity in tri neuron network with time delay this brief reports a retarded functional differential equation modeling tri neuron network with time delay the bogdanov takens b t bifurcation is investigated by using the center manifold reduction and the normal form method we get the versal unfolding of the norm forms at the b t singularity and show that the model can exhibit pitchfork hopf homoclinic and double limit cycles bifurcations some numerical simulations are given to support the analytic results and explore chaotic dynamics finally an algorithm is given to show that chaotic tri neuron networks can be used for encrypting a color image