equilibrium and stability analysis of delayed neural networks under parameter uncertainties abstract this paper proposes new results for the existence uniqueness and global asymptotic stability of the equilibrium point for neural networks with multiple time delays under parameter uncertainties by using lyapunov stability theorem and applying homeomorphism mapping theorem new delay independent stability criteria are obtained the obtained results are in terms of network parameters of the neural system only and therefore they can be easily checked we also present some illustrative numerical examples to demonstrate that our result are new and improve corresponding results derived in the previous literature

stochastic stability of neutral type markovian jumping bam neural networks with time varying delays abstract in this paper stochastic stability of neutral type markovian jumping bidirectional associative memory bam neural networks is investigated the jumping parameters are modeled as a continuous time discrete state markov chain the activation functions are supposed to be bounded and globally lipschitz continuous furthermore based on the lyapunov krasovskii functional a linear matrix inequality lmi approach is developed to establish sufficient conditions and novel delay dependent conditions are established for the stochastic asymptotic stability of markovian jumping bam neural networks the condition is presented in terms of linear matrix inequalities lmis which can be easily checked by using matlab lmi toolbox finally numerical examples are provided to show the effectiveness of the main results

iterative image reconstruction algorithms based on cross entropy minimization the related problems of minimizing the functionals f x alpha kl y px 1 alpha kl p x and g x alpha kl px y 1 alpha kl x p respectively over the set of vectors x or 0 are considered kl a b is the cross entropy or kullback leibler distance between two nonnegative vectors a and b iterative algorithms for minimizing both functionals using the method of alternating projections are derived a simultaneous version of the multiplicative algebraic reconstruction technique mart algorithm called smart is introduced and its convergence is proved

an interior point method with bregman functions for the variational inequality problem with paramonotone operators we present an algorithm for the variational inequality problem on convex sets with nonempty interior the use of bregman functions whose zone is the convex set allows for the generation of a sequence contained in the interior without taking explicitly into account the constraints which define the convex set we establish full convergence to a solution with minimal conditions upon the monotone operatorf weaker than strong monotonicity or lipschitz continuity for instance and including cases where the solution needs not be unique we apply our algorithm to several relevant classes of convex sets including orthants boxes polyhedra and balls for which bregman functions are presented which give rise to explicit iteration formulae up to the determination of two scalar stepsizes which can be found through finite search procedures 1998 the mathematical programming society inc published by elsevier science b v

bifurcations of travelling wave solutions in a model of the hydrogen bonded systems by using the theory of bifucations of dynamical systems to a model of the hydrogen bonded systems the existence of solitary wave kink and anti kink wave solutions and uncountably infinite many smooth and non smooth periodic wave solutions is obtained under different parametric conditions various sufficient conditions to guarantee the existence of the above solutions are given in some simple parametric conditions exact explicit and implicit solution formulas are listed

bifurcations of travelling wave solutions for modified nonlinear dispersive phi four equation abstract by using the bifurcation theory of dynamical systems to modified nonlinear dispersive phi four equation we analysis all bifurcations and phase portraits in the parametric space the existence of solitary wave solutions and uncountably infinite many smooth and non smooth periodic wave solutions is obtained under different parametric conditions various sufficient conditions to guarantee the existence of the above solutions are given some explicit exact solution formulas are acquired for some special cases

optimization of log x entropy over linear equality constraints in this paper we develop a special purpose iterative algorithm of the row action type for solving the problem of maximizing the log x entropy functional over linear equality constraints the algorithm employs projections onto hyperplanes which we call log x entropy projections a complete proof of convergence is given

global robust stability for stochastic interval neural networks with continuously distributed delays of neutral type in this paper by using the lyapunov krasovskii functional method we investigate the global robust stability for stochastic interval neural networks with continuously distributed delays of neutral type some new stability criteria are presented in terms of linear matrix inequality lmi two numerical examples are also given to show the effectiveness of the obtained results using lmi control toolbox in matlab

simplified exponential stability analysis for recurrent neural networks with discrete and distributed time varying delays this paper provides simplified exponential stability criteria for a class of recurrent neural networks rnns with discrete and distributed time varying delays the activation functions of the rnns are assumed to be more general and the proposed criteria are obtained by only using a integral inequality and are not involved any free weighting matrices this feature makes the computational burden largely reduced numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method

mean square global asymptotic stability of stochastic recurrent neural networks with distributed delays by constructing suitable lyapunov functionals and combining with matrix inequality technique a new simple sufficient condition is presented for the global asymptotic stability in the mean square of delayed neural networks

synchronization in an array of linearly stochastically coupled networks with time delays abstract in this paper the complete synchronization problem is investigated in an array of linearly stochastically coupled identical networks with time delays the stochastic coupling term which can reflect a more realistic dynamical behavior of coupled systems in practice is introduced to model a coupled system and the influence from the stochastic noises on the array of coupled delayed neural networks is studied thoroughly based on a simple adaptive feedback control scheme and some stochastic analysis techniques several sufficient conditions are developed to guarantee the synchronization in an array of linearly stochastically coupled neural networks with time delays finally an illustrate example with numerical simulations is exploited to show the effectiveness of the theoretical results

stable analysis for neural networks set valued mapping method by constructing some complete metric spaces this paper investigates the stable problem for a class of neural networks on the basis of set valued mapping theory by introducing the notion of essential equilibrium point some sufficient and necessary criteria guaranteeing the existence and stability of the equilibrium point are established when the activation function and uncertain parameter vary continuously different from most of previous works on the stability problem of neural network the feature of the method used in this paper does not depend on any lyapunov functional

iterative deblurring for ct metal artifact reduction iterative deblurring methods using the expectation maximization em formulation and the algebraic reconstruction technique art respectively are adapted for metal artifact reduction in medical computed tomography ct in experiments with synthetic noise free and additive noisy projection data of dental phantoms it is found that both simultaneous iterative algorithms produce superior image quality as compared to filtered backprojection after linearly fitting projection gaps furthermore the em type algorithm converges faster than the art type algorithm in terms of either the i divergence or euclidean distance between ideal and reprojected data in the authors simulation also for a given iteration number the em type deblurring method produces better image clarity but stronger noise than the art type reconstruction the computational complexity of em and art based iterative deblurring is essentially the same dominated by reprojection and backprojection relevant practical and theoretical issues are discussed

bifurcation studies on travelling wave solutions for nonlinear intensity klein gordon equation abstract in this paper we study the travelling waves for the n 1 dimensional klein gordon equation with nonlinear intensity by using the bifurcation theory and methods of planar dynamic systems with the aid of maple we obtain all bifurcations and phase portraits in the parametric space moreover we explain the reason for appearance of nonsmooth travelling waves and for persistence of smoothness of smooth travelling waves and show the existence of smooth and nonsmooth solitary and periodic waves under different regions of parametric space various sufficient conditions to guarantee the existence of the above solutions are given

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

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

a novel result on stability analysis for uncertain neutral stochastic time varying delay systems generalized finsler lemma is applied to deduce delay dependent stability conditions the case is considered that the discrete delay is different from the neutral delay model transformation cross terms estimation and free weighting matrices are avoided the conditions show less conservatism and involves fewer computational variables numerical examples show that the proposed approach improves some existing ones this paper is concerned with the mean square exponential stability analysis for uncertain neutral linear stochastic time varying delay systems by lyapunov krasovskii theory and linear matrix inequality method under the generalized finsler lemma gfl framework delay dependent mean square exponential stability criteria are established without involving model transformation cross terms bounding technique or additional free weighting matrix moreover gfl is also employed to obtain stability criteria for a class of uncertain linear stochastic neutral systems with different discrete and neutral delays numerical examples are presented to verify that the proposed approach is both less conservative and less computationally complex than the existing results

the primal dual algorithm as a constraint set manipulation device a general primal dual algorithm for linearly constrained optimization problems is formulated in which the dual variables are updated by a dual algorithmic operator convergence is proved under the assumption that the dual algorithmic operator implies asymptotic feasibility of the primal iterates with respect to the linear constraints a general result relating the minimal values of an infinite sequence of constrained problems to the minimal value of a limiting problem constrained by the limit of the sequence of constraints sets is established and invoked the applicability of the general theory is demonstrated by analyzing a specific dual algorithmic operator this leads to the mart algorithm for constrained entropy maximization used in image reconstruction from projections

entropy in linear programs this paper treats entropy constrained linear programs from modelling as well as computational aspects the optimal solutions to linear programs with one additional entropy constraint are expressed in terms of lagrange multipliers conditions for uniqueness are given sensitivity and duality are studied the newton kantorovich method is used to obtain a locally convergent iterative procedure related problems based on maximum entropy or minimum information are discussed

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

a new 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

row action methods for huge and sparse systems and their applications this paper brings together and discusses theory and applications of methods identified and labelled as row action methods for linear feasibility problems find x in bf r n such that ax leqq b linearly constrained optimization problems minimize f x subject to ax leqq b and some interval convex programming problems minimize f x subject to c leqq ax leqq b the main feature of row action methods is that they are iterative procedures which without making any changes to the original matrix a use the rows of a one row at a time such methods are important and have demonstrated effectiveness for problems with large or huge matrices which do not enjoy any detectable or usable structural pattern apart from a high degree of sparaseness fields of application where row action methods are used in various ways include image reconstruction from projection operations research and game theory learning theory pattern recognition and transportation theory a row action method for the

dynamical behaviors of a large class of general delayed neural networks research of delayed neural networks with varying self inhibitions interconnection weights and inputs is an important issue in the real world self inhibitions interconnection weights and inputs should vary as time varies in this letter we discuss a large class of delayed neural networks with periodic inhibitions interconnection weights and inputs we prove that if the activation functions are of lipschitz type and some set of inequalities for example the set of inequalities 3 1 in theorem 1 is satisfied the delayed system has a unique periodic solution and any solution will converge to this periodic solution we also prove that if either set of inequalities 3 20 in theorem 2 or 3 23 in theorem 3 is satisfied then the system is exponentially stable globally this class of delayed dynamical systems provides a general framework for many delayed dynamical systems as special cases it includes delayed hopfield neural networks and cellular neural networks as well as distributed delayed neural networks with periodic self inhibitions interconnection weights and inputs moreover the entire discussion applies to delayed systems with constant self inhibitions interconnection weights and inputs

globally exponential stabilization of neural networks with mixed time delays via impulsive control the impulsive stabilization problem of neural networks with discrete time varying delays and unbounded continuously distributed delays is considered by using impulse time dependent lyapunov function based techniques to capture the hybrid structure characteristics of the considered impulsive neural networks two novel global exponential stability criteria are obtained in terms of linear matrix inequalities which are capable of dealing with the case where both the continuous and discrete dynamics are unstable when the original continuous time delayed neural networks are not stable sufficient conditions are developed for the design of exponentially stable linear impulsive state feedback controllers four numerical examples are given to illustrate the less conservatism and effectiveness of the proposed 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

global asymptotic stability of stochastic recurrent neural networks with multiple discrete delays and unbounded distributed delays abstract in this paper using lyapunov krasovskii functional and the linear matrix inequality lmi approach the global asymptotic stability of stochastic recurrent neural networks with multiple discrete time varying delays and distributed delays is analyzed a new sufficient condition ensuring the global asymptotic stability for delayed recurrent neural networks is obtained in the stochastic sense using the powerful matlab lmi toolbox two examples are provided to illustrate the applicability of the stability results

smooth and non smooth travelling wave solutions of generalized degasperis procesi equation abstract the dynamical behavior and special exact solutions of the generalized degasperis procesi equation u t c 0 u x u txx auu x 3 u x u xx uu xxx is analyzed by using bifurcation theory and the method of phase portraits analysis as a result the analytic expressions of peakon solutions compacton solutions and periodic cusp wave solutions are obtained and the condition under which peakon and compacton solutions appear are also given in the meantime the reason that solitary cusp wave and periodic cusp wave appear is given

exponential stability of stochastic delayed hopfield neural networks the exponential stability for stochastic delayed hopfield neural networks hnns is considered the sufficient conditions to guarantee the almost surely exponential stability and exponential stability in mean square of an equilibrium solution are given one example is also given to demonstrate our results

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