stability analysis of markovian jump stochastic bam neural networks with impulse control and mixed time delays this paper discusses the issue of stability analysis for a class of impulsive stochastic bidirectional associative memory neural networks with both markovian jump parameters and mixed time delays the jumping parameters are modeled as a continuous time discrete state markov chain based on a novel lyapunov krasovskii functional the generalized ito s formula mathematical induction and stochastic analysis theory a linear matrix inequality approach is developed to derive some novel sufficient conditions that guarantee the exponential stability in the mean square of the equilibrium point at the same time we also investigate the robustly exponential stability in the mean square of the corresponding system with unknown parameters it should be mentioned that our stability results are delay dependent which depend on not only the upper bounds of time delays but also their lower bounds moreover the derivatives of time delays are not necessarily zero or smaller than one since several free matrices are introduced in our results consequently the results obtained in this paper are not only less conservative but also generalize and improve many earlier results finally two numerical examples and their simulations are provided to show the effectiveness of the theoretical results

fuzzy preferences in the graph model for conflict resolution a new framework for the graph model for conflict resolution is developed so that decision makers dms with fuzzy preferences can be included in conflict models a graph model is both a formal representation for multiple participant multiple objective decision problems and a set of analysis procedures that add insights into them within the new framework graph models can include and integrate into the analysis both certain and uncertain information about dms preferences one key contribution of this study is to extend the four basic stability definitions for two or more dms to models with fuzzy preferences together fuzzy nash stability fuzzy general metarationality fuzzy symmetric metarationality and fuzzy sequential stability provide anuanced description of human behavior a state is fuzzy stable for a dm if a move to any other state is not sufficiently likely to yield an outcome which the dm prefers where sufficiency is measured according to a fuzzy satisficing threshold that is the characteristic of the dm a fuzzy equilibrium which is an outcome that is fuzzy stable for all dms therefore represents a possible resolution of the strategic conflict the practical application and interpretation of these new stability definitions are illustrated with an example

random walk models for multifractals reversible random walk models for bernoulli multifractals in dimension 2 together with marginal versions of these are first studied to account for both geometrical and analytical properties of such objects using large deviation theory it is also shown how the transience recurrence of a more complex reversible random walk is related to the minimum energy flow convergence divergence property new results on the asymptotics of such a walker are then derived in both transient recurrent situations

paths and trails in edge colored graphs this paper deals with the existence and search for properly edge colored paths trails between two not necessarily distinct vertices s and t in an edge colored graph from an algorithmic perspective first we show that several versions of the s t path trail problem have polynomial solutions including the shortest path trail case we give polynomial algorithms for finding a longest properly edge colored path trail between s and t for a particular class of graphs and characterize edge colored graphs without properly edge colored closed trails next we prove that deciding whether there exist k pairwise vertex edge disjoint properly edge colored s t paths trails in a c edge colored graph g c is np complete even for k 2 and c w n 2 where n denotes the number of vertices in g c moreover we prove that these problems remain np complete for c edge colored graphs containing no properly edge colored cycles and c w n we obtain some approximation results for those maximization problems together with polynomial results for some particular classes of edge colored graphs

attribute conflict and preference uncertainty the randmau model this paper extends the behavioral results reported in fischer et al 2000 by developing a model addressing preference uncertainty in multiattribute evaluation the model is motivated by two hypotheses regarding properties of multiattribute profiles that lead to greater preference uncertainty our attribute conflict hypothesis predicts that greater within alternative conflict discrepancy among the attributes of an alternative leads to more preference uncertainty our attribute extremity hypothesis predicts that greater attribute extremity very high or low attribute values leads to less preference uncertainty to provide a deeper explanation of attribute conflict and extremity effects we develop randmau a family of additive randauf and multiplicative randmuf random weights multiattribute utility models in randmau models preference uncertainty is represented as random variation in both the weighting parameters governing trade offs among attributes and the curvature parameters governing single attribute evaluations simulation results show that randmuf successfully predicts both the attribute conflict and attribute extremity effects exhibited by the experimental participants in fischer et al 2000 it also predicts an outcome value effect on error whose form depends on the shape of single attribute functions and on the type of multiattribute combination rule

impulsive effects on stability of cohen grossberg neural networks with variable delays in this paper a model of impulsive cohen grossberg neural networks is first formulated by establishing some impulsive differential inequalities we investigate impulsive effects on the stability of cohen grossberg neural networks with variable delays and obtain some sufficient conditions ensuring global exponential stability of the impulsive delay system our criteria not only show that the stability still remains under certain impulsive perturbations for some continuous stable neural networks but also present an approach to stabilize the unstable neural networks by utilizing impulsive effects the results extend and improve some recent works for impulsive neural networks as well as non impulsive neural networks some examples and their simulations are given for illustration of the theoretical results

synchronization for coupled neural networks with interval delay a novel augmented lyapunov krasovskii functional method this paper is concerned with the synchronization problems for an array of neural networks with hybrid coupling and interval time varying delay first a novel augmented lyapunov krasovskii functional lkf method is proposed to develop delay dependent synchronization criteria for the networks which makes use of more relaxed conditions by employing the new type of augmented matrices with kronecker product operation the proposed method can handle a multitude of kronecker product operations in the lkf and alleviates the requirements of the positive definiteness of some conditional matrices which are usually considered in the existing methods for complex networks this leads to a significant improvement in the performance of the synchronization criteria i e less conservative synchronization results can be obtained meanwhile the case of fast time varying delay can also be handled by the proposed method furthermore based on the derived criteria a robust synchronization criterion is obtained for the system with uncertainties both in coefficient and coupling matrix terms since an expression based on linear matrix inequality is used the proposed criteria can be easily checked in practice finally numerical examples are provided to show the effectiveness of the proposed method

new approach to synchronization analysis of linearly coupled ordinary differential systems in this paper a general framework is presented for analyzing the synchronization stability of linearly coupled ordinary differential equations lcodes the uncoupled dynamical behavior at each node is general and can be chaotic or otherwise the coupling configuration is also general with the coupling matrix not assumed to be symmetric or irreducible on the basis of geometrical analysis of the synchronization manifold a new approach is proposed for investigating the stability of the synchronization manifold of coupled oscillators in this way criteria are obtained for both local and global synchronization these criteria indicate that the left and right eigenvectors corresponding to eigenvalue zero of the coupling matrix play key roles in the stability analysis of the synchronization manifold furthermore the roles of the uncoupled dynamical behavior on each node and the coupling configuration in the synchronization process are also studied

stability analysis and design of impulsive control systems with time delay a class of impulsive control systems with time varying delays is considered by establishing an impulsive delay differential inequality we analyze the global exponential stability of the impulsive delay systems and estimate the exponential convergence rate on the basis of the analysis a design procedure of impulsive controller is presented the designed impulsive controller not only can globally exponentially stabilize the time delay systems but also can control the exponential convergence rate of the systems two numerical examples are given to illustrate the effectiveness of the method

lower bounds on the proportion of leaders needed for expected consensus of 3 d flocks this paper considers the consensus behavior of a spatially distributed 3 d dynamical network composed of heterogeneous agents leaders and followers in which the leaders have the preferred information about the destination while the followers do not have all followers move in a 3 d euclidean space with a given speed and with their headings updated according to the average velocity of the corresponding neighbors compared with the 2 d model a key point lies in how to analyze the dynamical behavior of a linear nonhomogeneous equation where the nonhomogeneous term strongly nonlinearly depends on the states of all agents using the network structure and the estimation of some characteristics for the initial states we present a proper decaying rate for the nonhomogeneous term and then establish lower bounds on the ratio of the number of leaders to the number of followers that is needed for the expected consensus by considering two cases 1 fixed speed and neighborhood radius and 2 variable speed and neighborhood radius with respect to the population size some simulation examples are given to justify the 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

polymers on disordered hierarchical lattices a nonlinear combination of random variables the problem of directed polymers on disordered hierarchical and hypercubic lattices is considered for the hierarchical lattices the problem can be reduced to the study of the stable laws for combining random variables in a nonlinear way we present the results of numerical simulations of two hierarchical lattices finding evidence of a phase transition in one case for a limiting case we extend the perturbation theory developed by derrida and griffiths to nonzero temperature and to higher order and use this approach to calculate thermal and geometrical properties overlaps of the model in this limit we obtain an interpolation formula allowing one to obtain the noninteger moments of the partition function from the integer moments we obtain bounds for the transition temperature for hierarchical and hypercubic lattices and some similarities between the problem on the two different types of lattice are discussed

radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization this paper presents a novel algorithm for training radial basis function rbf networks in order to produce models with increased accuracy and parsimony the proposed methodology is based on a nonsymmetric variant of the fuzzy means fm algorithm which has the ability to determine the number and locations of the hidden node rbf centers whereas the synaptic weights are calculated using linear regression taking advantage of the short computational times required by the fm algorithm we wrap a particle swarm optimization pso based engine around it designed to optimize the fuzzy partition the result is an integrated framework for fully determining all the parameters of an rbf network the proposed approach is evaluated through its application on 12 real world and synthetic benchmark datasets and is also compared with other neural network training techniques the results show that the rbf network models produced by the pso based nonsymmetric fm algorithm outperform the models produced by the other techniques exhibiting higher prediction accuracies in shorter computational times accompanied by simpler network structures

matrix representation and extension of coalition analysis in group decision support coalition stability is extended to include uncertain preference using matrix representation within the framework of the graph model for conflict resolution coalition stability analysis within a graph model provides guidance for decision makers and analysts by providing an assessment of whether states that are individually stable are unstable for coalitions in using the graph model method for conflict resolution one carries out stability analysis of a graph model and then follows up with post stability analysis an important component of which is coalition analysis although basic coalition stabilities have been defined for the graph model they are based on a transitive graph which is inconsistent with the standard restriction in the graph model algorithms for implementing these coalition stabilities have not been developed because the nature of their logical representations makes coding difficult additionally existing coalition stabilities apply only to simple preference which limits their utility for exploring more complicated applications in practice in this paper coalition stabilities are extended to include uncertain preference and represented using matrices which are more effective and convenient for computer implementation and for adaptation to new analysis techniques

synchronization control for nonlinear stochastic dynamical networks pinning impulsive strategy in this paper a new control strategy is proposed for the synchronization of stochastic dynamical networks with nonlinear coupling pinning state feedback controllers have been proved to be effective for synchronization control of state coupled dynamical networks we will show that pinning impulsive controllers are also effective for synchronization control of the above mentioned dynamical networks some generic mean square stability criteria are derived in terms of algebraic conditions which guarantee that the whole state coupled dynamical network can be forced to some desired trajectory by placing impulsive controllers on a small fraction of nodes an effective method is given to select the nodes which should be controlled at each impulsive constants the proportion of the controlled nodes guaranteeing the stability is explicitly obtained and the synchronization region is also derived and clearly plotted numerical simulations are exploited to demonstrate the effectiveness of the pinning impulsive strategy proposed in this paper

estimating utility functions in the presence of response error this paper explores the nature and extent of response error when direct multiattribute utility assessment procedures are used as a basis for modeling preferences for risky multiattribute alternatives the analysis is based on an experimental study of preferences for alternative air pollution control policies whose consequences were characterized by three value attributes cost to consumers level of pollution related illness and level of pollution related mortality the study generated the following findings i direct assessments of preferences for outcomes were quite reliable and stable over a two week time period ii parameter estimates for additive utility functions fitted to direct utility assessments were both precise and stable over a two week time period iii statistically fitted additive utility models provided very accurate predictions of directly assessed preferences two weeks later or earlier iv ranking outcomes before assigning utilities to them resulted in high levels of serial co

an extension of labeling techniques for finding shortest path trees label setting techniques are all based on dijkstra s condition of always scanning the node with the minimum label which guarantees that each node will be scanned exactly once while this condition is sufficient it is not necessary in this paper we discuss less restrictive conditions that allow the scanning of a node that does not have the minimum label yet still maintaining sufficiency in scanning each node exactly once various potential shortest path schemes are discussed based on these conditions two approaches a label setting and a flexible hybrid one are designed and implemented the performance of the algorithms is assessed both theoretically and computationally for comparative analysis purposes three additional shortest path algorithms the commonly cited in the literature are coded and tested the results indicate that the approaches that rely on the less restrictive optimality conditions perform substantially better for a wide range of network topologies

stability analysis and application for delayed neural networks driven by fractional brownian noise this paper deals with two types of the stability problem for the delayed neural networks driven by fractional brownian noise fbn the existence and the uniqueness of the solution to the main system with respect to fbn are proved via fixed point theory based on hilbert schmidt operator theory and analytic semigroup principle the mild solution of the stochastic neural networks is obtained by applying the stochastic analytic technique and some well known inequalities the asymptotic stability criteria and the exponential stability condition are established both numerical example and practical application for synchronization control of multiagent system are provided to illustrate the effectiveness and potential of the proposed techniques

nonfragile exponential synchronization of delayed complex dynamical networks with memory sampled data control this paper considers nonfragile exponential synchronization for complex dynamical networks cdns with time varying coupling delay the sampled data feedback control which is assumed to allow norm bounded uncertainty and involves a constant signal transmission delay is constructed for the first time in this paper by constructing a suitable augmented lyapunov function and with the help of introduced integral inequalities and employing the convex combination technique a sufficient condition is developed such that the nonfragile exponential stability of the error system is guaranteed as a result for the case of sampled data control free of norm bound uncertainties some sufficient conditions of sampled data synchronization criteria for the cdns with time varying coupling delay are presented as the formulations are in the framework of linear matrix inequality these conditions can be easily solved and implemented two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control

stabilizing effects of impulses in discrete time delayed neural networks this brief studies the global exponential stability of the equilibrium point of discrete time delayed hopfield neural networks dhnns with impulse effects by using difference inequalities we shall consider the stabilizing effects of impulses when the corresponding impulse free dhnn is even not asymptotically stable the obtained results characterize the aggregated effects of impulses and deviation of the impulse free dhnn from its equilibrium point on the exponential stability of the whole system it is shown that because of effects of impulses the impulsive discrete time dhnn may be exponentially stable even if the evolution of impulse free component deviates from its equilibrium point exponentially

pinning stabilization of linearly coupled stochastic neural networks via minimum number of controllers pinning stabilization problem of linearly coupled stochastic neural networks lcsnns is studied in this paper a minimum number of controllers are used to force the lcsnns to the desired equilibrium point by fully utilizing the structure of the network in order to pinning control the lcsnns to a certain desired state only one controller is required for strongly connected network topology and m controllers which will be shown to be the minimum number are needed for lcsnns with m reducible coupling matrix the isolate node of the lcsnns can be stable periodic or even chaotic the coupling laplacian matrix of the lcsnns can be symmetric irreducible asymmetric irreducible or m reducible which means that the network topology can be strongly connected weakly connected or even unconnected there is no constraint on the network topology some criteria are derived to judge whether the lcsnns can be controlled in mean square by using designed controllers the given criteria are expressed in terms of strict linear matrix inequalities which can be easily checked by resorting to recently developed algorithm moreover numerical examples including small world and scale free networks are also given to demonstrate that our theoretical results are valid and efficient for large systems

robust exponential stability of markovian jump impulsive stochastic cohen grossberg neural networks with mixed time delays this paper is concerned with the problem of exponential stability for a class of markovian jump impulsive stochastic cohen grossberg neural networks with mixed time delays and known or unknown parameters the jumping parameters are determined by a continuous time discrete state markov chain and the mixed time delays under consideration comprise both time varying delays and continuously distributed delays to the best of the authors knowledge till now the exponential stability problem for this class of generalized neural networks has not yet been solved since continuously distributed delays are considered in this paper the main objective of this paper is to fill this gap by constructing a novel lyapunov krasovskii functional and using some new approaches and techniques several novel sufficient conditions are obtained to ensure the exponential stability of the trivial solution in the mean square the results presented in this paper generalize and improve many known results finally two numerical examples and their simulations are given to show the effectiveness of the theoretical results

global synchronization for discrete time stochastic complex networks with randomly occurred nonlinearities and mixed time delays in this paper the problem of stochastic synchronization analysis is investigated for a new array of coupled discrete time stochastic complex networks with randomly occurred nonlinearities rons and time delays the discrete time complex networks under consideration are subject to 1 stochastic nonlinearities that occur according to the bernoulli distributed white noise sequences 2 stochastic disturbances that enter the coupling term the delayed coupling term as well as the overall network and 3 time delays that include both the discrete and distributed ones note that the newly introduced rons and the multiple stochastic disturbances can better reflect the dynamical behaviors of coupled complex networks whose information transmission process is affected by a noisy environment e g internet based control systems by constructing a novel lyapunov like matrix functional the idea of delay fractioning is applied to deal with the addressed synchronization analysis problem by employing a combination of the linear matrix inequality lmi techniques the free weighting matrix method and stochastic analysis theories several delay dependent sufficient conditions are obtained which ensure the asymptotic synchronization in the mean square sense for the discrete time stochastic complex networks with time delays the criteria derived are characterized in terms of lmis whose solution can be solved by utilizing the standard numerical software a simulation example is presented to show the effectiveness and applicability of the proposed results

using a bayesian approach to quantify scale compatibility bias this paper proposes a new analytical framework to quantify and correct for scale compatibility bias in the assessment of trade off weights in multiattribute value analysis the procedure is demonstrated with an application to a fisheries management problem trade off judgments are elicited from a group of fisheries experts with management responsibility in the lake erie basin then we use a bayesian method to compute posterior probability distributions of attribute weights in computing the bayesian weights our measurement model assumes that the weight ratios produced by each respondent s judgments are subject to random error and an unknown scale compatibility bias ratios are log transformed and analyzed by a bayesian linear model with a noninformative prior distribution posterior distributions are then developed for the weights and the bias we estimate the compatibility bias for each person and in most cases it is found to be large and in the predicted direction suggesting the importance of its consideration in deriving trade off weights in addition the bayesian framework is shown to be useful for quantifying the value of additional information about multiattribute weights finally a simple heuristic procedure for assessing the weights appears to be effective in eliminating the bias

on hybrid impulsive and switching systems and application to nonlinear control in this note a new class of hybrid impulsive and switching models is introduced and their asymptotic stability properties are investigated using switched lyapunov functions some new general criteria for exponential stability and asymptotic stability with arbitrary and conditioned impulsive switching are established in addition a new hybrid impulsive and switching control strategy for nonlinear systems is developed a typical example the unified chaotic system is given to illustrate the theoretical results

pinning distributed synchronization of stochastic dynamical networks a mixed optimization approach this paper is concerned with the problem of pinning synchronization of nonlinear dynamical networks with multiple stochastic disturbances two kinds of pinning schemes are considered 1 pinned nodes are fixed along the time evolution and 2 pinned nodes are switched from time to time according to a set of bernoulli stochastic variables using lyapunov function methods and stochastic analysis techniques several easily verifiable criteria are derived for the problem of pinning distributed synchronization for the case of fixed pinned nodes a novel mixed optimization method is developed to select the pinned nodes and find feasible solutions which is composed of a traditional convex optimization method and a constraint optimization evolutionary algorithm for the case of switching pinning scheme upper bounds of the convergence rate and the mean control gain are obtained theoretically simulation examples are provided to show the advantages of our proposed optimization method over previous ones and verify the effectiveness of the obtained results

the interactive analysis of the multicriteria shortest path problem by the reference point method abstract the multicriteria shortest path problem is considered the paper presents the interactive method of analyzing this problem by the reference point approach the reference point guided labeling algorithm was developed this algorithm finds the pareto optimal shortest path which is best attuned to the specified preferences

coalition analysis with preference uncertainty in group decision support coalition analysis is extended to incorporate uncertain preference into three stability concepts general metarationality gmr symmetric metarationality smr and sequential stability seq under the paradigm of the graph model for conflict resolution as a follow up analysis in the graph model coalition analysis aims to assess whether equilibriums under individual calculations are vulnerable to coalition moves and countermoves and hence become unstable under coalition stabilities coalition analysis has been considered for transitive graph models with simple preference under four stabilities nash gmr smr and seq as well as general graph models with uncertain preference for the nash stability this paper introduces preference uncertainty into coalition stabilities under gmr smr and seq for general graph models that can be transitive or intransitive depending on the focal coalition s different attitudes towards preference uncertainty four different extensions are presented interrelationships of coalition stabilities are investigated within each extension and across the four extensions a case study is carried out to illustrate how to apply the proposed coalition stabilities

stability analysis based on partition trajectory approach for switched neural networks with fractional brown noise disturbance abstractin this brief the stability problem based on feedback control for two types of stochastic neural networks driven by fractional brown noise is considered one class is the switched neural networks without time delays and the other one is with time delays a novel analysis method very different to the usual approach based on the ito formula and infinitesimal operator is proposed in this paper by the idea of splitting time of trajectory and associating with holder inequality some criteria are obtained to guarantee the switched neural networks with two types to be stable in the end two numerical examples and auxiliary figures are presented to show the feasibility and effectiveness for the proposed 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

strength of preference in graph models for multiple decision maker conflicts abstract models of strength of preference are incorporated into the graph model for conflict resolution to study realistically multi objective decision making situations in disputes with any finite number of participants a decision maker s preference is expressed using a triplet of binary relations that allows preference of one state over another to be strong or weak and also allows indifference four basic stability definitions used in the graph model to represent human behavior in conflicts are extended to the context of the new triplet preference structure producing strong and weak stabilities theorems governing the relationships among standard strong and weak stabilities are presented finally application of the new preference representation to an international water resource management conflict confirms the practical utility of the triplet structure and the significance of the strategic insights it can provide

exponential synchronization of neural networks with discrete and distributed delays under time varying sampling this paper investigates the problem of master slave synchronization for neural networks with discrete and distributed delays under variable sampling with a known upper bound on the sampling intervals an improved method is proposed which captures the characteristic of sampled data systems some delay dependent criteria are derived to ensure the exponential stability of the error systems and thus the master systems synchronize with the slave systems the desired sampled data controller can be achieved by solving a set of linear matrix inequalitys which depend upon the maximum sampling interval and the decay rate the obtained conditions not only have less conservatism but also have less decision variables than existing results simulation results are given to show the effectiveness and benefits of the proposed methods

an integrated algebraic approach to conflict resolution with three level preference an integrated algebraic approach is developed to calculate stabilities in multiple decision maker graph models with three levels of preference the algebraic approach establishes an integrated paradigm for stability analysis and status quo analysis under different preference structures such as two level preference and three level preference difficulties in coding algorithms to analyze stabilities rooted in their logical representation led to the development of matrix representations of preference and explicit matrix calculations to determine stabilities here the algebraic approach is used to represent graph models with three levels of preference and to conduct stability analysis for such models the algebraic approach facilitates the development of new stability concepts and algorithms to calculate them and reveals an inherent link between status quo analysis and traditional stability analysis hence it will facilitate the design of an integrated decision support system for the graph model for conflict resolution

technical communique static output feedback stabilization an ilmi approach in this note the static output feedback stabilization problem is addressed using the linear matrix inequality technique a necessary and sufficient condition for static output feedback stabilizability for linear time invariant systems is derived in the form of a matrix inequality the extension of the result to h control is studied an iterative lmi ilmi algorithm is proposed to compute the feedback gain numerical examples are employed to demonstrate the effectiveness and the convergence of the algorithm

on some stability problems of impulsive stochastic cohen grossberg neural networks with mixed time delays abstract this paper covers the topic of both the p th moment p 2 and almost sure stability of impulsive stochastic cohen grossberg neural networks with mixed time delays we partially use a known result on exponential stability of impulsive stochastic functional differential systems based on the razumikhin type technique and extend it to the case of stochastic neural networks using the lyapunov function method and a gronwall type inequality additionally we consider the stability with respect to a general decay function which includes exponential but also more general lower rate decay functions as the polynomial and the logarithmic ones this fact gives us the opportunity to study general decay almost sure stability even when the exponential one cannot be discussed suitable examples which support the theory are also presented

using matrices to link conflict evolution and resolution in a graph model abstract the graph model for conflict resolution provides a convenient and effective means to model and analyze a strategic conflict standard practice is to carry out a stability analysis of a graph model and then to follow up with a post stability analysis an important component of which is status quo analysis a graph model can be viewed as an edge colored graph but the fundamental problem of status quo analysis to find a shortest colored path from the status quo node to a desired equilibrium is different from the well known network analysis problem of finding the shortest path between two nodes the only matrix method that has been proposed cannot track all aspects of the evolution of a conflict from the status quo state our explicit algebraic approach is convenient for computer implementation and as demonstrated with a real world case study easy to use it provides new insights into a graph model not only identifying all equilibria reachable from the status quo but also how to reach them moreover this approach bridges the gap between stability analysis and status quo analysis in the graph model for conflict resolution

inverse engineering preferences in simple games a method for inverse engineering decision makers preferences based on observable behaviour is designed this technique allows analysts to narrow down the list of potential preference rankings of possible states in a conflict for each decision maker using probabilities and expected values during the inverse engineering procedure the list of all possible preference rankings is narrowed as decision makers move and counter move accurate preference information is key to building quality conflict and game models however preference rankings for decision makers are often difficult to obtain directly a simple two decision maker four state game is used to demonstrate the applicability of the method and to illustrate the insights it provides

fuzzy levels of preference strength in a graph model with multiple decision makers abstract a new hybrid preference structure combining multiple level strength of preference and fuzzy preference is proposed to facilitate the modeling and analysis of strategic conflicts involving multiple decision makers dms with complex preferences using the graph model for conflict resolution gmcr the new preference structure named fuzzy strength of preference provides a more comprehensive and flexible representation of dms relative preferences among states a key contribution of this paper is to redefine four graph model stability definitions for fuzzy preferences fuzzy nash stability fnash fuzzy general metarationality fgmr fuzzy symmetric metarationality fsmr and fuzzy sequential stability fseq as general stabilities strong stabilities and weak stabilities at each level permitting deeper analysis of graph models with the new preference structure the resulting methodology can be utilized to model and analyze complex multiple dm conflicts thereby enhancing the capability of the graph model to provide strategic insights a graph model of the zhanghe river water allocation conflict in china demonstrates that the method can find the evolution path of a conflict and give new strategic insights for both practitioners and researchers the fuzzy strength of preference framework makes gmcr more capable for addressing a wider range of practical conflicts

consensus seeking in multiagent systems under dynamically changing interaction topologies this note considers the problem of information consensus among multiple agents in the presence of limited and unreliable information exchange with dynamically changing interaction topologies both discrete and continuous update schemes are proposed for information consensus this note shows that information consensus under dynamically changing interaction topologies can be achieved asymptotically if the union of the directed interaction graphs have a spanning tree frequently enough as the system evolves

stability analysis of impulsive stochastic cohen grossberg neural networks with mixed time delays abstract in this paper the problem of stability analysis for a class of impulsive stochastic cohen grossberg neural networks with mixed delays is considered the mixed time delays comprise both the time varying and infinite distributed delays by employing a combination of the m matrix theory and stochastic analysis technique a sufficient condition is obtained to ensure the existence uniqueness and exponential p stability of the equilibrium point for the addressed impulsive stochastic cohen grossberg neural network with mixed delays the proposed method which does not make use of the lyapunov functional is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and or distributed delays we then extend our main results to the case where the parameters contain interval uncertainties moreover the exponential convergence rate index is estimated which depends on the system parameters an example is given to show the effectiveness of the obtained results

robust synchronization of an array of coupled stochastic discrete time delayed neural networks this paper is concerned with the robust synchronization problem for an array of coupled stochastic discrete time neural networks with time varying delay the individual neural network is subject to parameter uncertainty stochastic disturbance and time varying delay where the norm bounded parameter uncertainties exist in both the state and weight matrices the stochastic disturbance is in the form of a scalar wiener process and the time delay enters into the activation function for the array of coupled neural networks the constant coupling and delayed coupling are simultaneously considered we aim to establish easy to verify conditions under which the addressed neural networks are synchronized by using the kronecker product as an effective tool a linear matrix inequality lmi approach is developed to derive several sufficient criteria ensuring the coupled delayed neural networks to be globally robustly exponentially synchronized in the mean square the lmi based conditions obtained are dependent not only on the lower bound but also on the upper bound of the time varying delay and can be solved efficiently via the matlab lmi toolbox two numerical examples are given to demonstrate the usefulness of the proposed synchronization scheme

distributed consensus algorithms in sensor networks quantized data and random link failures the paper studies the problem of distributed average consensus in sensor networks with quantized data and random link failures to achieve consensus dither small noise is added to the sensor states before quantization when the quantizer range is unbounded countable number of quantizer levels stochastic approximation shows that consensus is asymptotically achieved with probability one and in mean square to a finite random variable we show that the mean squared error mse can be made arbitrarily small by tuning the link weight sequence at a cost of the convergence rate of the algorithm to study dithered consensus with random links when the range of the quantizer is bounded we establish uniform boundedness of the sample paths of the unbounded quantizer this requires characterization of the statistical properties of the supremum taken over the sample paths of the state of the quantizer this is accomplished by splitting the state vector of the quantizer in two components one along the consensus subspace and the other along the subspace orthogonal to the consensus subspace the proofs use maximal inequalities for submartingale and supermartingale sequences from these we derive probability bounds on the excursions of the two subsequences from which probability bounds on the excursions of the quantizer state vector follow the paper shows how to use these probability bounds to design the quantizer parameters and to explore tradeoffs among the number of quantizer levels the size of the quantization steps the desired probability of saturation and the desired level of accuracy away from consensus finally the paper illustrates the quantizer design with a numerical study

pinning consensus in networks of multiagents via a single impulsive controller in this paper we discuss pinning consensus in networks of multiagents via impulsive controllers in particular we consider the case of using only one impulsive controller we provide a sufficient condition to pin the network to a prescribed value it is rigorously proven that in case the underlying graph of the network has spanning trees the network can reach consensus on the prescribed value when the impulsive controller is imposed on the root with appropriate impulsive strength and impulse intervals interestingly we find that the permissible range of the impulsive strength completely depends on the left eigenvector of the graph laplacian corresponding to the zero eigenvalue and the pinning node we choose the impulses can be very sparse with the impulsive intervals being lower bounded examples with numerical simulations are also provided to illustrate the theoretical results

pinning synchronization in t s fuzzy complex networks with partial and discrete time couplings communication constraints which may lead to the degradation of performance are common and unavoidable in a real world network in this paper two kinds of communication constraints are considered during the process of information transmission in takagi sugeno ts fuzzy complex network 1 partial couplings where only some part of nodes state information can be transmitted and the channel matrices are introduced to reflect such a phenomenon and 2 discrete time couplings where the nodes information are sampled at certain time instants this is the first time when both communication constraints are simultaneously considered in fuzzy complex networks compared with the perfect communication much less information is available for synchronization to overcome this difficulty a regrouping method is employed to reconstruct the fuzzy network the concise conditions are then proposed to ensure pinning synchronization of fuzzy complex networks with partial and discrete time couplings simulation examples are also provided to demonstrate the effectiveness of the theoretical results

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

group decisions with multiple criteria we consider a decision problem where a group of individuals evaluates multiattribute alternatives we explore the minimal required agreements that are sufficient to specify the group utility function a surprising result is that under some conditions a bilateral agreement among pairs of individuals on a single attribute is sufficient to derive the multiattribute group utility the bilateral agreement between a pair of individuals could be on the weight of an attribute on an attribute evaluation function or on willingness to pay we focus on the case in which each individual s utility function is additive we show that the group utility can be represented as the weighted sum of group attribute weights and more remarkably of attribute evaluation functions these group attribute evaluation functions are in turn weighted sums of individual attribute evaluation functions

common asymptotic behavior of solutions and almost periodicity for discontinuous delayed and impulsive neural networks the paper considers a general neural network model with impulses at a given sequence of instants discontinuous neuron activations delays and time varying data and inputs it is shown that when the neuron interconnections satisfy an m matrix condition or a dominance condition then the state solutions and the output solutions display a common asymptotic behavior as time t it is also shown via a new technique based on prolonging the solutions of the delayed neural network to that it is possible to select a unique special solution that is globally exponentially stable and can be considered as the unique global attractor for the network finally this paper shows that for almost periodic data and inputs the selected solution is almost periodic moreover it is robust with respect to a large class of perturbations of the data analogous results also hold for periodic data and inputs a by product of the analysis is that a sequence of almost periodic impulses is able to induce in the generic case nonstationary almost periodic solutions in an otherwise globally convergent nonimpulsive neural network to the authors knowledge the results in this paper are the only available results on global exponential stability of the unique periodic or almost periodic solution for a general neural network model combining three main features i e impulses discontinuous neuron activations and delays the results in this paper are compared with several results in the literature dealing with periodicity or almost periodicity of some subclasses of the neural network model here considered and some hints for future work are given

the edge recoloring cost of monochromatic and properly edge colored paths and cycles we introduce a number of problems regarding edge color modifications in edge colored graphs and digraphs consider a property a c edge colored graph g c not satisfying and an edge recoloring cost matrix r r i j c c where r i j 0 denotes the cost of changing color i of edge e to color j basically in this kind of problem the idea is to change the colors of one or more edges of g c in order to construct a new edge colored graph such that the total edge recoloring cost is minimized and property is satisfied we also consider the destruction of potentially undesirable structures with the minimum edge recoloring cost in this paper we are especially concerned with the construction and destruction of properly edge colored and monochromatic paths trails and cycles in graphs and digraphs some related problems and future directions are presented

a new binary relation to compare viability of winning coalitions and its interrelationships to desirability relation and blockability relation this paper aims to propose a new type of binary relations called the viability relation defined on the set of all coalitions in a simple game for a comparison of coalition influence and to investigate its properties especially its interrelationships to the desirability relation and the blockability relation the viability relation is defined to compare coalitions based on their robustness over deviation of their members for complementing the inability of the desirability relation and the blockability relation to make a distinguishable comparison among winning coalitions it is verified in this paper that the viability relation on a simple game is always transitive and is complete if and only if the simple game is s unanimous for a coalition s examples show that there are no general inclusion relations among the desirability relation the blockability relation and the viability relation it is also verified that the viability relation and the blockability relation are complementary to each other specifically the blockability relation between two coalitions is equivalent to the inversed viability relation between the complements of the two coalitions

a matrix based approach to searching colored paths in a weighted colored multidigraph an algebraic approach to finding all edge weighted colored paths within a weighted colored multidigraph is developed generally the adjacency matrix represents a simple digraph and determines all paths between any two vertices and is not readily extendable to colored multidigraphs to bridge the gap a conversion function is proposed to transform the original problem of searching edge colored paths in a colored multidigraph to a standard problem of finding paths in a simple digraph moreover edge weights can be used to represent some preference attribute its potentially wide realm of applicability is illustrated by a case study status quo analysis in the graph model for conflict resolution the explicit matrix function is more convenient than other graphical representations for computer implementation and for adapting to other applications additionally the algebraic approach reveals the relationship between a colored multidigraph and a simple digraph thereby providing new insights into algebraic graph theory

conflict models in graph form solution concepts and their interrelationships abstract a comprehensive approach for modeling conflicts is the recently developed graph form of conflict models within the flexible structure of the graph model solution concepts including nash stability the various metagame techniques the sequential stability method of fraser and hipel and stackelberg equilibrium are defined and characterized these stability principles are compared with the limited move and non myopic stabilities developed previously for the graph model framework and mathematical interrelationships in this very general context are derived as well some steps are taken toward the development of a taxonomy of solution concepts for the graph form

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

multifractal like features of the paths probability distribution in the two dimensional directed random walk abstract we consider the two dimensional directed random walk with probabilities px and py assigned to steps in x and y direction respectively and analyse the probability distribution for the possible 2t paths the walker may follow when it performs a t step walk this distribution is multifractal since its qth moment has the typical power law behaviour of multifractal distributions unless p x p y 1 2 if the values of px and py are allowed to fluctuate around their average values and throughout the lattice the qth moment averaged over the possible realizations of the lattice exhibits the multifractal power law behaviour even when x y 1 2 the existence of negative fractal dimensions is also analysed

graph theory based pinning synchronization of stochastic complex dynamical networks this paper is concerned with the adaptive pinning synchronization problem of stochastic complex dynamical networks cdns based on algebraic graph theory and lyapunov theory pinning controller design conditions are derived and the rigorous convergence analysis of synchronization errors in the probability sense is also conducted compared with the existing results the topology structures of stochastic cdn are allowed to be unknown due to the use of graph theory in particular it is shown that the selection of nodes for pinning depends on the unknown lower bounds of coupling strengths finally an example on a chua s circuit network is given to validate the effectiveness of the theoretical results