global existence and blowup of a localized problem with free boundary abstract this paper is concerned with a double fronts free boundary problem for the heat equation with a localized nonlinear reaction term the local existence and uniqueness of the solution are given by applying the contraction mapping theorem then we present some conditions so that the solution blows up in finite time finally the long time behavior of the global solution is discussed we show that the solution is global and fast if the initial data is small and that a global slow solution is possible when the initial data is suitably large

global asymptotic stability of recurrent neural networks with multiple time varying delays in this paper several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time varying delays the lyapunov krasovskii stability theory for functional differential equations and the linear matrix inequality lmi approach are employed in our investigation the results are shown to be generalizations of some previously published results and are less conservative than existing results the present results are also applied to recurrent neural networks with constant time delays

new stability results for delayed neural networks this paper is concerned with the stability for delayed neural networks by more fully making use of the information of the activation function a new lyapunov krasovskii functional lkf is constructed then a new integral inequality is developed and more information of the activation function is taken into account when the derivative of the lkf is estimated by lyapunov stability theory a new stability result is obtained finally three examples are given to illustrate the stability result is less conservative than some recently reported ones

large time behavior of the solution of the heat equation with nonlinear strong absorption abstract we give an estimate for the asymptotic behavior of the solution of a semilinear heat equation and of its free boundary

spreading and vanishing in a diffusive prey predator model with variable intrinsic growth rate and free boundary abstract we study the spreading and vanishing phenomena in a diffusive prey predator system with variable intrinsic growth rate and free boundary in this model the free boundary represents the spreading front and is caused only by the prey and the variable intrinsic growth rate is allowed to tend to zero and decay very fast as t or x our main attention is on the effect of variable intrinsic growth rate on the solution and attempt to find some new techniques to deal with the variable intrinsic growth rate we first study the long time behavior of u v for the vanishing case h then we find the criteria for spreading and vanishing at last the long time behavior of u v for the spreading case h is discussed theorem 2 2 theorem 2 3 theorem 4 1 together establish a spreading vanishing dichotomy

relating the slope of the activation function and the learning rate within a recurrent neural network a relationship between the learning rate in the learning algorithm and the slope fl in the nonlinear activation function for a class of recurrent neural networks rnns trained by the real time recurrent learning algorithm is provided it is shown that an arbitrary rnn can be obtained via the referent rnn with some deterministic rules imposed on its weights and the learning rate such relationships reduce the number of degrees of freedom when solving the nonlinear optimization task of finding the optimal rnn parameters

stability for neural networks with time varying delays via some new approaches this paper considers the problem of delay dependent stability criteria for neural networks with time varying delays first by constructing a newly augmented lyapunov krasovskii functional a less conservative stability criterion is established in terms of linear matrix inequalities second by proposing novel activation function conditions which have not been proposed so far further improved stability criteria are proposed finally three numerical examples used in the literature are given to show the improvements over the existing criteria and the effectiveness of the proposed idea

on a free boundary problem for a reaction diffusion advection logistic model in heterogeneous environment abstract in this paper we investigate a reaction diffusion advection equation with a free boundary which models the spreading of an invasive species in one dimensional heterogeneous environments we assume that the species has a tendency to move upward along the resource gradient in addition to random dispersal and the spreading mechanism of species is determined by a stefan type condition investigating the sign of the principal eigenvalue of the associated linearized eigenvalue problem under certain conditions we obtain the sharp criteria for spreading and vanishing via system parameters also we establish the long time behavior of the solution and the asymptotic spreading speed finally some biological implications are discussed

a note on sargan densities abstract this note reconsiders the general class of sargan densities studied by goldfeld and quandt 1981 and missiakoulis 1983 in the context of approximating normal densities in regression models some errors in these papers are pointed out and the sargan densities are re computed to the contrary of missiakoulis conclusion the first two order densities are not the best choice higher order densities are better approximations though the best choice appears to be the second order density based on the goldfeld quandt criteria

global existence and blowup of a nonlocal problem in space with free boundary abstract this paper concerns a double fronts free boundary problem for the reaction diffusion equation with a nonlocal nonlinear reaction term in space for such a problem we mainly study the blowup property and global existence of the solutions our results show that if the initial value is sufficiently large then the blowup occurs while the global fast solution exists for a sufficiently small initial data and the intermediate case with a suitably large initial data gives the existence of the global slow solution

admissible delay upper bounds for global asymptotic stability of neural networks with time varying delays this paper is concerned with global asymptotic stability of a neural network with a time varying delay where the delay function is differentiable uniformly bounded with delay derivative bounded from above first a general reciprocally convex inequality is presented by introducing some slack vectors with flexible dimensions this inequality provides a tighter bound in the form of a convex combination than some existing ones second by constructing proper lyapunov krasovskii functional global asymptotic stability of the neural network is analyzed for two types of the time varying delays depending on whether or not the lower bound of the delay derivative is known third noticing that sufficient conditions on stability from estimation on the derivative of some lyapunov krasovskii functional are affine both on the delay function and its derivative allowable delay sets can be refined to produce less conservative stability criteria for the neural network under study finally two numerical examples are given to substantiate the effectiveness of the proposed method

absolute exponential stability of recurrent neural networks with generalized activation function in this paper the recurrent neural networks rnns with a generalized activation function class is proposed in this proposed model every component of the neuron s activation function belongs to a convex hull which is bounded by two odd symmetric piecewise linear functions that are convex or concave over the real space all of the convex hulls are composed of generalized activation function classes the novel activation function class is not only with a more flexible and more specific description of the activation functions than other function classes but it also generalizes some traditional activation function classes the absolute exponential stability aest of the rnn with a generalized activation function class is studied through three steps the first step is to demonstrate the global exponential stability ges of the equilibrium point of original rnn with a generalized activation function being equivalent to that of rnn under all vertex functions of convex hull the second step transforms the rnn under every vertex activation function into neural networks under an array of saturated linear activation functions because the ges of the equilibrium point of three systems are equivalent the next stability analysis focuses on the ges of the equilibrium point of rnn system under an array of saturated linear activation functions the last step is to study both the existence of equilibrium point and the ges of the rnn under saturated linear activation functions using the theory of m matrix in the end a two neuron rnn with a generalized activation function is constructed to show the effectiveness of our results

finite time dissipative control for stochastic interval systems with time delay and markovian switching the finite time stochastically boundedness ftsb and the finite time strictly stochastically exponential dissipative ftssed control problems for the stochastic interval systems which are encountered the time delay and markovian switching are investigated in this paper the stochastic delayed interval systems with markovian switching sdisswms are equivalently transformed into a kind of stochastic uncertain time delay systems with markovian switching by interval matrix transformation some sufficient conditions of ftsb and ftssed for the stochastic delayed interval systems with markovian switching are obtained and the ftsb and ftssed controllers are designed by solving a series of linear matrix inequalities which are solvable by lmis toolbox finally a numerical example with simulations is given to illustrate the correctness of the obtained results and the effectiveness of the designed controller

wirtinger based integral inequality application to time delay systems in the last decade the jensen inequality has been intensively used in the context of time delay or sampled data systems since it is an appropriate tool to derive tractable stability conditions expressed in terms of linear matrix inequalities lmis however it is also well known that this inequality introduces an undesirable conservatism in the stability conditions and looking at the literature reducing this gap is a relevant issue and always an open problem in this paper we propose an alternative inequality based on the fourier theory more precisely on the wirtinger inequalities it is shown that this resulting inequality encompasses the jensen one and also leads to tractable lmi conditions in order to illustrate the potential gain of employing this new inequality with respect to the jensen one two applications on time delay and sampled data stability analysis are provided

spreading vanishing dichotomy in a diffusive logistic model with a free boundary ii abstract we study the diffusive logistic equation with a free boundary in higher space dimensions and heterogeneous environment such a model may be used to describe the spreading of a new or invasive species with the free boundary representing the expanding front for simplicity we assume that the environment and the solution are radially symmetric in the special case of one space dimension and homogeneous environment this free boundary problem was investigated in du and lin 2010 10 we prove that the spreading vanishing dichotomy established in du and lin 2010 10 still holds in the more general and ecologically realistic setting considered here moreover when spreading occurs we obtain best possible upper and lower bounds for the spreading speed of the expanding front when the environment is asymptotically homogeneous at infinity these two bounds coincide our results indicate that the asymptotic spreading speed determined by this model does not depend on the spatial dimension

delay slope dependent stability results of recurrent neural networks by using the fact that the neuron activation functions are sector bounded and nondecreasing this brief presents a new method named the delay slope dependent method for stability analysis of a class of recurrent neural networks with time varying delays this method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed lyapunov krasovskii functional then some improved delay dependent stability criteria with less computational burden and conservatism are obtained numerical examples are given to illustrate the effectiveness and the benefits of the proposed method

novel inequality with application to improve the stability criterion for dynamical systems with two additive time varying delays abstract in this paper the stability of the system with two additive time varying delay components is studied and improved stability condition is obtained it firstly establishes two novel integral inequalities which are better than the same type inequalities found in the literature secondly a new constructed lyapunov functional is constructed based on the additive time varying delays property following two steps to handle the lyapunov functional the delay dependent stability condition is obtained which in terms of linear matrix inequalities finally two numerical examples are given to verify the effectiveness of the proposed method and the superiority of the results

new delay dependent stability criteria for neural networks with time varying delay in this letter a new method is proposed for stability analysis of neural networks nns with a time varying delay some less conservative delay dependent stability criteria are established by considering the additional useful terms which were ignored in previous methods when estimating the upper bound of the derivative of lyapunov functionals and introducing the new free weighting matrices numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

on the stability of linear systems with uncertain delay this paper focuses on the stability of some class of delay systems including uncertainty in the delays more precisely we are interested in guaranteeing the stability of perturbed delay systems by assuming the stability of the nominal system if the delay perturbation is constant necessary and sufficient conditions are derived in terms of generalized eigenvalue distribution of some finite dimensional constant matrix pencil if the delay perturbation is time varying some sufficient stability conditions are derived using exact lyapunov krasovskii functionals illustrative examples are also included

a note on sargan densities abstract this note reexamines the general class of sargan densities analyzed by goldfeld and quandt 1981 kafei and schmidt 1985 and others in the context of approximating normal densities in certain econometric models it suggests a third order sargan density which complies with the goldfeld and quandt criteria is unimodal and is the best approximation to the normal for orders less than or equal to 3 some errors in goldfeld and quandt paper are corrected

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

an augmented model for robust stability analysis of time varying delay systems stability analysis of linear systems with time varying delay is investigated in order to highlight the relations between the variation of the delay and the states redundant equations are introduced to construct a new modelling of the delay system new types of lyapunov krasovskii functionals are then proposed allowing to reduce the conservatism of the stability criterion delay dependent stability conditions are then formulated in terms of linear matrix inequalities finally several examples show the effectiveness of the proposed methodology

new delay dependent stability results for neural networks with time varying delay this paper studies the problem of stability analysis for neural networks nns with a time varying delay unlike the previous works the activation functions are assumed to be neither monotonic nor differentiable nor bounded by defining a more general type of lyapunov functionals some new less conservative delay dependent stability criteria are established in terms of linear matrix inequalities lmis meanwhile the computational complexity of the newly obtained stability conditions is reduced because less variables are involved numerical examples are given to illustrate the effectiveness and the benefits of the proposed method

econometric modelling with non normal disturbances abstract this paper analyzes a general class of non normal density functions dubbed sargan densities in the context of the ordinary regression model and the simple one market disequilibrium model use of the normal density in disequilibrium models is unwieldy especially for multimarket models since the application of maximum likelihood methods requires numerical evaluation of multiple integrals these difficulties are avoided with the sargan densities and based on both asymptotic results and limited sampling experiments these densities appear to offer a promising alternative to the normal in disequilibrium models

stability analysis of fractional order complex valued neural networks with both leakage and discrete delays in this paper the problem of uniform stability for fractional order complex valued neural networks with both leakage and discrete delays is considered base on the contracting mapping principle a sufficient condition is proposed for the existence and uniqueness of the equilibrium point of the addressed neural networks by employing analysis technique some delay dependent criteria are established for checking the global uniform stability of the fractional order complex valued neural networks two numerical examples are presented to demonstrate the validity and feasibility of the proposed results

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

reciprocally convex approach to stability of systems with time varying delays whereas the upper bound lemma for matrix cross product introduced by park 1999 and modified by moon park kwon and lee 2001 plays a key role in guiding various delay dependent criteria for delayed systems the jensen inequality has become an alternative as a way of reducing the number of decision variables it directly relaxes the integral term of quadratic quantities into the quadratic term of the integral quantities resulting in a linear combination of positive functions weighted by the inverses of convex parameters this paper suggests the lower bound lemma for such a combination which achieves performance behavior identical to approaches based on the integral inequality lemma but with much less decision variables comparable to those based on the jensen inequality lemma

global asymptotic stability of delayed cellular neural networks a new criterion for the global asymptotic stability of the equilibrium point of cellular neural networks with multiple time delays is presented the obtained result possesses the structure of a linear matrix inequality and can be solved efficiently using the recently developed interior point algorithm a numerical example is used to show the effectiveness of the obtained result

sargan densities which one abstract goldfeld and quandt 1981 suggest the use of sargan distribution as a viable alternative to normal distribution in econometric models this paper examines the resemblance of a sargan type family of densities to the normal density and concludes that the first two order cases are the best choice

new and improved results on stability of static neural networks with interval time varying delays abstract in this paper the problem of stability analysis for static neural networks with interval time varying delays is considered by the consideration of new augmented lyapunov functionals new and improved delay dependent stability criteria to guarantee the asymptotic stability of the concerned networks are proposed with the framework of linear matrix inequalities lmis which can be solved easily by standard numerical packages the enhancement of the feasible region of the proposed criteria is shown via two numerical examples by the comparison of maximum delay bounds

on improved delay dependent criterion for global stability of bidirectional associative memory neural networks with time varying delays in the paper the global robust asymptotic stability of bidirectional associative memory bam neural networks with time varying delays and uncertainties is investigated a novel stability criterion is presented based on the lyapunov method the criterion is represented in terms of linear matrix inequality lmi which can be solved easily by various optimization algorithms a numerical example is illustrated to show the effectiveness of our new result

misspecification tests in econometrics based on ranks abstract this paper introduces a class of rank statistics for use in testing econometric models for misspecification these statistics are ideally suited to survey data since endogenous and exogenous variables in econometric models may therefore be considered exchangeable thus residuals from a wide class of models e g linear regression non linear regression simultaneous equations etc are also exchangeable since rank statistics are distribution free when based on exchangeable data it follows the rank statistics based on these residuals are also distribution free simulation studies in the linear regression case suggest that they have good power characteristics and are distributionally robust in comparison to the usual classical procedures

the formation of the dead core in parabolic reaction diffusion problems on donne des conditions pour l existence d une region a concentration de reactant nulle pour des problemes paraboliques non lineaires de reaction diffusion

on some free boundary problems of the prey predator model abstract in this paper we investigate some free boundary problems for the lotka volterra type prey predator model in one space dimension the main objective is to understand the asymptotic behavior of the two species prey and predator spreading via a free boundary we prove a spreading vanishing dichotomy namely the two species either successfully spread to the entire space as time t goes to infinity and survive in the new environment or they fail to establish and die out in the long run the long time behavior of solution and criteria for spreading and vanishing are also obtained finally when spreading successfully we provide an estimate to show that the spreading speed if exists cannot be faster than the minimal speed of traveling wavefront solutions for the prey predator model on the whole real line without a free boundary

asymptotic behavior of the solutions of the dead core problem u u pup 0 1 1 this equation originates from the study of a class of models for the reaction diffusion processes of a gas inside a chemical reactor 2 81 from the mathematical point of view the major interest of equation 1 1 lies in the competition of the linear diffusion and the nonlinear fast p 0 t 0 with initial and boundary conditions

stability of systems with several monotone nonlinearities this paper is devoted to stability problems of automatic control systems frequency domain criteria for global asymptotic stability of equilibrium sets for new classes of nonlinearities are derived results are illustrated by 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

the structure of simultaneous equation estimators a generalization towards nonnormal disturbances a general linear simultaneous equation system with a multivariate student t disturbance vector is considered the normal equations of the corresponding maximum likelihood estimator are used as estimator generating equations to introduce a new class of estimators properties of large subclasses of these estimators are determined for disturbance vectors other than the multivariate student t

exponential stability of time delay systems via new weighted integral inequalities in this paper new weighted integral inequalities wiis are first derived based on jensen s integral inequalities in single and double forms it is theoretically shown that the newly derived inequalities in this paper encompass both the jensen inequality and its most recent improvement based on wirtinger s integral inequality the potential capability of wiis is demonstrated through applications to exponential stability analysis of some classes of time delay systems in the framework of linear matrix inequalities lmis the effectiveness and least conservativeness of the derived stability conditions using wiis are shown by various numerical examples

stability analysis for delayed neural networks considering both conservativeness and complexity this paper investigates delay dependent stability for continuous neural networks with a time varying delay this paper aims at deriving a new stability criterion considering tradeoff between conservativeness and calculation complexity a new lyapunov krasovskii functional with simple augmented terms and delay dependent terms is constructed and its derivative is estimated by several techniques including free weighting matrix and inequality estimation methods then the influence of the techniques used on the conservativeness and the complexity is analyzed one by one moreover useful guidelines for improving criterion and future work are briefly discussed finally the advantages of the proposed criterion compared with the existing ones are verified based on three numerical examples

improved delay dependent asymptotic stability criteria for delayed neural networks this brief is concerned with asymptotic stability of neural networks with uncertain delays two types of uncertain delays are considered one is constant while the other is time varying the discretized lyapunov krasovskii functional lkf method is integrated with the technique of introducing the free weighting matrix between the terms of the leibniz newton formula the integrated method leads to the establishment of new delay dependent sufficient conditions in form of linear matrix inequalities for asymptotic stability of delayed neural networks dnns a numerical simulation study is conducted to demonstrate the obtained theoretical results which shows their less conservatism than the existing stability criteria

biased movement and the ideal free distribution in some free boundary problems abstract some invasive species can persist in their habitat but eventually spread very slow in a nonlinear fashion to expand their habitat range in order to capture this phenomenon we consider reaction diffusion advection models with a free boundary modeling the spreading and the biased movement of species in one dimensional spatially heterogeneous environments under a condition of low resource quality we find that large advection can lead to the spreading of the species but the spreading speed goes asymptotically to zero moreover we investigate the effect of the resource on the dynamics of the current problem finally we bring the notion of an ideal free distribution ifd into free boundary problems to understand the mechanism such that the species can eventually match the environmental quality perfectly under the current problem setting the ifd may not hold even if the population plays an ideal free strategy we then provide a sufficient condition for the ifd to be reached when using an ideal free strategy

stability analysis of static recurrent neural networks with interval time varying delay the problem of stability analysis of static recurrent neural networks with interval time varying delay is investigated in this paper a new lyapunov functional which contains some new double integral and triple integral terms are introduced information about the lower bound of the delay is fully used in the lyapunov functional integral and double integral terms in the derivative of the lyapunov functional are divided into some parts to get less conservative results some sufficient stability conditions are obtained in terms of linear matrix inequality lmi numerical examples are given to illustrate the effectiveness of the proposed method

a further refinement of discretized lyapunov functional method for the stability of time delay systems the discretized lyapunov functional method for the stability problem of time delay systems is further refined using a combination of integral inequality and variable elimination technique as a result the computational requirement is further reduced for the same discretization mesh for systems without uncertainty the convergence to the analytical solution is greatly accelerated for uncertain systems the new method is much less conservative numerical examples are presented to illustrate the effectiveness of the method

delay dependent stability for recurrent neural networks with time varying delays this brief is concerned with the stability for static neural networks with time varying delays delay independent conditions are proposed to ensure the asymptotic stability of the neural network the delay independent conditions are less conservative than existing ones to further reduce the conservatism delay dependent conditions are also derived which can be applied to fast time varying delays expressed in linear matrix inequalities both delay independent and delay dependent stability conditions can be checked using the recently developed algorithms examples are provided to illustrate the effectiveness and the reduced conservatism of the proposed result

a free boundary problem for the diffusive intraguild predation model with intraspecific competition abstract in this paper we investigate a free boundary problem for the diffusive intraguild predation model with intraspecific competition in one dimensional space the main objective is to portray the asymptotic behavior of spread of an invasive or new predator species via a free boundary in this model we assume both the ig prey and the ig invasive new predator species with intraspecific competition and the ig predator species can only expand further into the new environment from the right end of the initial region in both cases we prove a spreading vanishing dichotomy for this model specifically the ig predator species either successfully spreads to infinity as t at the front and survives in the new environment or spreads within a bounded area and dies out in the long run the long time behavior of r n p and criteria for spreading and vanishing are also obtained

the free boundary problem describing information diffusion in online social networks abstract in this paper we consider a free boundary problem for a reaction diffusion logistic equation with a time dependent growth rate such a problem arises in the modeling of information diffusion in online social networks with the free boundary representing the spreading front of news among users we present several sharp thresholds for information diffusion that either lasts forever or suspends in finite time in the former case we give the asymptotic spreading speed which is determined by a corresponding elliptic equation

lmi based stability criteria for neural networks with multiple time varying delays abstract in this paper the stability of neural networks with multiple time varying delays is studied a new class of lyapunov krasovskii functionals is constructed and the s procedure and free weighting matrix method are employed to derive a delay dependent stability criterion from which a delay independent criterion is obtained as a special case moreover the result is also extended to delay dependent and rate independent stability criteria for multiple unknown time varying delays finally numerical examples are given to illustrate the effectiveness of our methods and improvement over the existing ones

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

a class of partially adaptive one step m estimators for the non linear regression model with dependent observations in this paper we consider a class of partially adaptive one step m estimators for the non linear regression model with dependent observations those estimators adapt themselves with respect to a measure of the tailthickness of the disturbance distribution as well as to a measure of the scale the large sample behavior of those estimators is examined theoretically for general disturbance distributions and numerically for various specific ones the estimators considered are motivated by the student r maximum likelihood estimator given appropriate specifications of the adaptation parameter the estimators are asymptotically efficient on the family of student t distributions including the normal distribution

short time behaviour of a singular solution to the heat equation with absorption on considere l evolution aux temps petits de l interface qui apparait dans le probleme aux limites u t u xx u p u o t 1 t 0 u x o 0 x 0 pour p 1 on construit une solution interieure d ordre zero consistante avec une interface mobile ou u est zero

partially adaptive estimation via a normal mixture abstract this paper proposes a new partially adaptive regression estimator the estimator is derived by modeling the disturbance distribution as a variance mixture of normal distributions yet the true disturbance distribution need not be a variance mixture in order for the proposed estimator to be consistent and asymptotically normal moreover the partially adaptive estimator is shown to be less sensitive to extreme values than the ordinary least squares estimator a point that is illustrated with some monte carlo experiments the paper also provides some easily programmed em algorithms for computing estimates

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