boundedness and exponential stability for nonautonomous rcnns with distributed delays some sufficient conditions for the ultimate boundedness and global exponential stability of a class of nonautonomous reaction diffusion cellular neural networks rcnns with distributed delays are obtained by means of the lyapunov functional method without assuming that the activation functions f i j l are bounded the results extend and improve the earlier publications

an improved global asymptotic stability criterion for delayed cellular neural networks a new lyapunov krasovskii functional is constructed for delayed cellular neural networks and the s procedure is employed to handle the nonlinearities an improved global asymptotic stability criterion is also derived that is a generalization of and an improvement over previous results numerical examples demonstrate the effectiveness of the criterion

implicit high order accurate in space algorithms for the navier stokes equations high order accurate alternating direction implicit schemes have been developed these schemes can be used to obtain efe ciently high resolution numerical solutions of the compressible euler and navier stokes equations implicit solvers compatible with both centered and upwind schemes for the discretization of the convective terms arediscussed forcenteredspacediscretizationoftheconvectivee uxes fourth orderspatialaccuracyoftheimplicit operators is obtained at no additional computing cost by performing compact space differentiation this implicit compact scheme which requires block tridiagonal matrix inversions is used to obtain viscous and inviscid e ow solutions in curvilinear coordinates in addition high order accurate in space implicit operators are obtained for diagonalized schemes which require scalar matrix inversions both high order algorithms improve convergence characteristics for subsonic and transonic e ow compared to second order accurate in space implicit schemes high order accurate in space implicit algorithms compatible with upwind high resolution shock capturing total variation diminishing schemes are also developed third order accurate upwind biased diagonalized algorithms were found to be as accurate and more efe cient than the standard block tridiagonal implicit solvers

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

comparison of implicit schemes for the incompressible navier stokes equations for a computational flow simulation tool to be useful in a design environment it must be very robust and efficient to develop such a tool for incompressible flow applications a number of different implicit schemes are compared for several two dimensional flow problems in the current study the schemes include point jacobi relaxation gauss seidel line relaxation incomplete lower upper decomposition and the generalized minimum residual method preconditioned with each of the three other schemes the efficiency of the schemes is measured in terms of the computing time required to obtain a steady state solution for the laminar flow over a backward facing step the flow over a naca 4412 airfoil and the flow over a three element airfoil using overset grids the flow solver used in the study is the ins2d code that solves the incompressible navier stokes equations using the method of artificial compressibility and upwind differencing of the convective terms the results show that the generalized minimum residual method preconditioned with the incomplete lower upper factorization outperforms all other methods by at least a factor of 2

delay dependent criteria for global robust periodicity of uncertain switched recurrent neural networks with time varying delay in this paper we introduce some ideas of switched systems into the field of neural networks and a large class of switched recurrent neural networks srnns with time varying structured uncertainties and time varying delay is investigated some delay dependent robust periodicity criteria guaranteeing the existence uniqueness and global asymptotic stability of periodic solution for all admissible parametric uncertainties are devised by taking the relationship between the terms in the leibniz newton formula into account because free weighting matrices are used to express this relationship and the appropriate ones are selected by means of linear matrix inequalities lmis the criteria are less conservative than existing ones reported in the literature for delayed neural networks with parameter uncertainties some examples are given to show that the proposed criteria are effective and are an improvement over previous ones

observer design for switched recurrent neural networks an average dwell time approach this paper is concerned with the problem of observer design for switched recurrent neural networks with time varying delay the attention is focused on designing the full order observers that guarantee the global exponential stability of the error dynamic system based on the average dwell time approach and the free weighting matrix technique delay dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities the error state decay estimate is also given then the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results finally four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods

output feedback control of switched nonlinear systems using multiple lyapunov functions abstract this work presents a hybrid nonlinear control methodology for a broad class of switched nonlinear systems with input constraints the key feature of the proposed methodology is the integrated synthesis via multiple lyapunov functions of lower level bounded nonlinear feedback controllers together with upper level switching laws that orchestrate the transitions between the constituent modes and their respective controllers both the state and output feedback control problems are addressed under the assumption of availability of full state measurements a family of bounded nonlinear state feedback controllers are initially designed to enforce asymptotic stability for the individual closed loop modes and provide an explicit characterization of the corresponding stability region for each mode a set of switching laws are then designed to track the evolution of the state and orchestrate switching between the stability regions of the constituent modes in a way that guarantees asymptotic stability of the overall switched closed loop system when complete state measurements are unavailable a family of output feedback controllers are synthesized using a combination of bounded state feedback controllers high gain observers and appropriate saturation filters to enforce asymptotic stability for the individual closed loop modes and provide an explicit characterization of the corresponding output feedback stability regions in terms of the input constraints and the observer gain a different set of switching rules based on the evolution of the state estimates generated by the observers is designed to orchestrate stabilizing transitions between the output feedback stability regions of the constituent modes the differences between the state and output feedback switching strategies and their implications for the switching logic are discussed and a chemical process example is used to demonstrate the proposed approach

new criteria for global robust stability of delayed neural networks with norm bounded uncertainties in this paper we study the global asymptotic robust stability of delayed neural networks with norm bounded uncertainties by employing the lyapunov stability theory and homeomorphic mapping theorem we derive some new types of sufficient conditions ensuring the existence uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slope bounded activation functions an important aspect of our results is their low computational complexity as the reported results can be verified by checking some properties of symmetric matrices associated with the uncertainty sets of the network parameters the obtained results are shown to be generalizations of some of the previously published corresponding results some comparative numerical examples are also constructed to compare our results with some closely related existing literature results

robust stabilizing control laws for a class of second order switched systems abstract for a class of second order switched systems consisting of two linear time invariant lti subsystems we show that the so called conic switching law proposed previously by the present authors is robust not only in the sense that the control law is flexible to be explained further but also in the sense that the lyapunov stability resp lagrange stability properties of the switched system are preserved in the presence of certain kinds of vanishing perturbations resp nonvanishing perturbations the analysis is possible since the conic switching laws always possess certain kinds of quasi periodic switching operations we also propose for a class of nonlinear second order switched systems with time invariant subsystems a switching control law which locally exponentially stabilizes the entire nonlinear switched system provided that the conic switching law exponentially stabilizes the linearized switched systems consisting of the linearization of each nonlinear subsystem this switched control law is robust in the sense mentioned above

passivity of switched recurrent neural networks with time varying delays this paper is concerned with the passivity analysis for switched neural networks subject to stochastic disturbances and time varying delays first using the multiple lyapunov functions method a state dependent switching law is designed to present a stochastic passivity condition second a hysteresis switching law involving both the current state and the previous value of the switching signal are presented to avoid chattering resulted from the state dependent switching third based on the average dwell time approach a class of switching signals is determined to guarantee the switched neural network stochastically passive finally three numerical examples are provided to illustrate the characteristics of three proposed switching laws

a compact difference scheme for the navier stokes equations in vorticity velocity formulation this paper presents a new numerical method for solving the incompressible unsteady navier stokes equations in vorticity velocity formulation the method is applicable to spatial simulations of transitional and turbulent boundary layer flows it is based on a compact difference discretization of the streamwise and wall normal derivatives in cartesian coordinates a fourier collocation approach is used for the spanwise derivatives important new features of the numerical method are the use of nonequidistant differences in the wall normal direction the use of split compact differences in the streamwise direction a new fast iteration for a semi implicit time integration of the wall normal diffusion terms and an improvement of the buffer domain technique to prevent reflections of waves at the outflow boundary results of test calculations are presented to verify the improvements obtained by the use of these new techniques

kernel estimation of risk surfaces without the need for edge correction kernel estimates of relative risk surfaces can be used to examine the geographical variation of disease risk these surfaces can be expressed as ratios of bivariate kernel density estimates constructed from case and control data but care must be taken to avoid excessive bias at the boundaries of the region under study it is possible to correct this bias without the complications of explicit edge correction through the use of a specific smoothing regimen

numerical algorithms for flows in the nodes of 2d models of pipe networks this work is aimed at developing efficient computational algorithms for numerical simulation of steady state incompressible viscous flows in nodes of circulation networks high order accuracy implicit stabilization algorithms based on upwind finite difference schemes were proposed in order to treat complicated geometry decomposition of computational domains was used the effect of an external circulation network was simulated by means of nonlocal boundary conditions linking inlet and outlet flow parameters a fluid flow related to specific cardiovascular operation was studied numerically as an example

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

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

novel global stability criteria for high order hopfield type neural networks with time varying delays abstract this paper discusses a generalized model of high order hopfield type neural networks with time varying delays some novel global stability criteria of the system is derived by using lyapunov method linear matrix inequality lmi and analytic technique the lmi based criteria obtained here are computationally more flexible and more generic than many other existing criteria a numerical example is given to illustrate our result

implicit unified gas kinetic scheme for steady state solutions in all flow regimes this paper presents an implicit unified gas kinetic scheme ugks for non equilibrium steady state flow computation the ugks is a direct modeling method for flow simulation in all regimes with the updates of both macroscopic flow variables and microscopic gas distribution function by solving the macroscopic equations implicitly a predicted equilibrium state can be obtained first through iterations with the newly predicted equilibrium state the evolution equation of the gas distribution function and the corresponding collision term can be discretized in a fully implicit way for fast convergence through iterations as well the lower upper symmetric gauss seidel lu sgs factorization method is implemented to solve both macroscopic and microscopic equations which improves the efficiency of the scheme since the ugks is a direct modeling method and its physical solution depends on the mesh resolution and the local time step a physical time step needs to be fixed before using an implicit iterative technique with a pseudo time marching step therefore the physical time step in the current implicit scheme is determined by the same way as that in the explicit ugks for capturing the physical solution in all flow regimes but the convergence to a steady state speeds up through the adoption of a numerical time step with large cfl number many numerical test cases in different flow regimes from low speed to hypersonic ones such as the couette flow cavity flow and the flow passing over a cylinder are computed to validate the current implicit method the overall efficiency of the implicit ugks can be improved by one or two orders of magnitude in comparison with the explicit one

guidelines for the usage of incomplete decompositions in solving sets of linear equations as they occur in practical problems abstract this paper presents incomplete decompositions for various types of matrices as they occur in the implicit discretisation of practical problems a review is given of methods for the usual five point discretisation of a self adjoint elliptic second order partial differential equation in two dimensions on a square the matrices which occur in this type of problem are symmetric m matrices of very regular structure the convergence behaviour of the different decompositions for this case is demonstrated by numerical experiments the paper also gives decompositions for the following type of matrices i symmetric m matrices of a different structure ii symmetric positive definite matrices iii non symmetric matrices

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

exponential stability and periodic oscillatory solution in bam networks with delays both exponential stability and periodic oscillatory solution of bidirectional associative memory bam networks with axonal signal transmission delays are considered by constructing suitable lyapunov functional and some analysis techniques some simple sufficient conditions are given ensuring the global exponential stability and the existence of periodic oscillatory solutions of bam with delays these conditions are presented in terms of system parameters and have important leading significance in the design and applications of globally exponentially stable and periodic oscillatory neural circuits for bam with delays in addition two examples are given to illustrate the results

producing stable periodic solutions of switched impulsive delayed neural networks using a matrix based cubic convex combination approach this article is dedicated to designing a novel periodic impulsive control strategy for producing globally exponentially stable periodic solutions for switched neural networks with discrete and finite distributed time varying delays first tunable parameters and cubic convex combination approach are proposed to study the globally exponential convergence of switched neural networks second a sufficient criterion for the existence uniqueness and globally exponential stability of a periodic solution is demonstrated by using contraction mapping theorem and the impulse delay dependent lyapunov krasovskii functional method it is worth emphasizing that the addressed lyapunov krasovskii functional covers both triple integral terms and novel quadruple integral terms which makes the conservatism of the above criteria decrease even if the original neural network models are unstable or the impulsive effects are strong the addressed neural network model can produce a globally exponentially stable periodic solution these results here which include boundedness globally uniformly exponential convergence and globally exponentially stability of the periodic solution generalize and improve the earlier publications finally two numerical examples and their computer simulations are given to show the effectiveness of theoretical results

stability in asymmetric hopfield nets with transmission delays sufficient conditions are derived for the delay independent stability of the equilibria in hopfield s graded response networks of the type dxi t dt bixi t nj 1aijfj jxj t ij fi t i 1 2 n when the external inputs fi are held temporally uniform a generalization to continuously distributed delays is briefly indicated several illustrative examples are numerically simulated and the results of simulations are graphically displayed