finite time event triggered mathcal h _ infty control for t s fuzzy markov jump systems this paper investigates the finite time event triggered mathcal h infty control problem for takagi sugeno markov jump fuzzy systems because of the sampling behaviors and the effect of network environment the premise variables considered in this paper are subject to asynchronous constraints the aim of this paper is to synthesize a controller via an event triggered communication scheme such that not only the resulting closed loop system is finite time bounded and satisfies a prescribed mathcal h infty performance level but also the communication burden is reduced first a sufficient condition is established for the finite time bounded mathcal h infty performance analysis of the closed loop fuzzy system with fully considering the asynchronous premises then based on the derived condition the method of the desired controller design is presented two illustrative examples are finally presented to demonstrate the practicability and efficacy of the proposed method

consensus in multi agent systems with random delays governed by a markov chain abstract this paper investigates the consensus problem in a multi agent system with random delays governed by a markov chain the communication topology is assumed to be directed and fixed with first order dynamics under the sampled data setting we first convert the original system into a reduced order one featuring the error dynamics accordingly the consensus problem is transformed into the stabilization of the error dynamic system thereafter based on the theory in stochastic stability for time delay systems a sufficient condition is established in terms of a set of linear matrix inequalities lmis the mean square stability of the error dynamics is shown to guarantee consensus of the multi agent system by explicitly incorporating the transition probability of the random delay into consideration the conservativeness in control design is reduced a delay dependent switching control scheme is developed by redesigning the adjacency matrix finally simulation results are provided to verify the effectiveness of the proposed approach

the randomization technique as a modeling tool and solution procedure for transient markov processes we present a randomization procedure for computing transient solutions to discrete state space continuous time markov processes this procedure computes transient state probabilities it is based on a construction relating a continuous time markov process to a discrete time markov chain modifications and extensions of the randomization method allow for computation of distributions of first passage times and sojourn times in markov processes and also the computation of expected cumulative occupancy times and expected number of events occurring during a time interval several implementations of the randomization procedure are discussed in particular we present an implementation for a general class of markov processes that can be described in terms of state space s event set e rate vectors r and target vectors t abbreviated as sert this general approach can handle systems whose state spaces are quite large if they have sparse generators

hierarchical gradient parameter estimation algorithm for hammerstein nonlinear systems using the key term separation principle in this paper we use the hierarchical identification principle to decompose a hammerstein controlled autoregressive system into three subsystems apply the key term separation principle to express the system output as a linear combination of the system parameters and then derive a hierarchical gradient parameter estimation algorithm for identifying all subsystems finally a multi innovation stochastic gradient algorithm is presented to improve the estimation accuracy by making full of the identification innovation the simulation results show that the proposed algorithm is effective

design of networked control systems with packet dropouts this note is concerned with stability and controller design of networked control systems ncss with packet dropouts new ncs models are provided considering both single and multiple packet transmissions both sensor to controller s c and controller to actuator c a packet dropouts are modeled and their history behavior is described by different independent markov chains in term of the given models sufficient conditions for stochastic stability are derived in the form of linear matrix inequalities lmis and corresponding control laws are given numerical examples illustrate the effectiveness of the results

performability asymptotic distribution and moment computation in this paper we analyze the asymptotic behavior of performability in a markov reward process an accurate and numerically stable method is proposed to compute the performability of tail distributions we derive quite simple comparatively to known results recursive formulae for the moments of performability a numerical example is considered to illustrate the use of our approach

performability measure for acyclic markovian models abstract continuous time markov processes with a finite state space are generally considered to model degradable fault tolerant computer systems the finite space is partitioned as m i 1 b i where b i stands for the set of states which corresponds to the configuration where the system has a performance level or reward rate equal to i the performability y t is defined as the accumulated reward over a mission time 0 t in this paper a renewal equation is established for the performability measure and solved for both standard and uniform acyclic models two closed form expressions for the performability measure are derived for the two types of models furthermore an algorithm with a low polynomial computational complexity is presented and applied to a degradable computer system

nonnested model comparisons for time series this paper addresses the topic of nonnested time series model comparisons the main result is a central limit theorem for the likelihood ratio statistic when the models are nonnested and non equivalent the concepts of model equivalence and forecast equivalence which are important for determining the parameter subset corresponding to the null hypothesis are developed the method is validated through a simulation study and illustrated on a retail time series

computation of matrix exponentials of special matrices computing matrix exponentials or transition matrices are important in signal processing and control systems this paper studies the methods of computing the transition matrices related to linear dynamic systems discusses and gives the properties of the transition matrices and explores the transition matrices of some special matrices some examples are provided to illustrate the proposed methods

asymptotic theory of cepstral random fields random fields play a central role in the analysis of spatially correlated data and as a result have a significant impact on a broad array of scientific applications given the importance of this topic there has been a substantial amount of research devoted to this area however the cepstral random field model remains largely underdeveloped outside the engineering literature we provide a comprehensive treatment of the asymptotic theory for two dimensional random field models in particular we provide recursive formulas that connect the spatial cepstral coefficients to an equivalent moving average random field which facilitates easy computation of the necessary autocovariance matrix additionally we establish asymptotic consistency results for bayesian maximum likelihood and quasi maximum likelihood estimation of random field parameters and regression parameters further in both the maximum and quasi maximum likelihood frameworks we derive the asymptotic distribution of our estimator the theoretical results are presented generally and are of independent interest pertaining to a wide class of random field models the results for the cepstral model facilitate model building because the cepstral coefficients are unconstrained in practice numerical optimization is greatly simplified and we are always guaranteed a positive definite covariance matrix we show that inference for individual coefficients is possible and one can refine models in a disciplined manner finally our results are illustrated through simulation and the analysis of straw yield data in an agricultural field experiment

asymptotically optimal estimation in misspecified time series models a concept of asymptotically efficient estimation is presented whena misspecified parametric time series model is fitted to a stationary process efficiency of several minimum distance estimates is proved and the behavior ofthe gaussian maximum likelihood estimate is studied furthermore the behaviorof estimates that minimize the h step prediction error is discussed briefly the paper answers to some extent the question what happens when a misspecifiedmodel is fitted to time series data and one acts as if the model were true

output feedback stabilization of networked control systems with random delays modeled by markov chains this note investigates the output feedback stabilization of networked control systems ncss the sensor to controller s c and controller to actuator c a random network induced delays are modeled as markov chains the focus is on the design of a two mode dependent controller that depends on not only the current s c delay but also the most recent available c a delay at the controller node the resulting closed loop system is transformed to a special discrete time jump linear system then the sufficient and necessary conditions for the stochastic stability are established further the output feedback controller is designed via the iterative linear matrix inequality lmi approach simulation examples illustrate the effectiveness of the proposed method

properties of predictors for autoregressive time series abstract the prediction of the n s th observation of the pth order autoregressive process is investigated the mean square of the predictor error through terms of order n 1 conditional on yn y n 1 y n p 1 is obtained for the stationary normal process the mean squared error expression is similar to the usual regression formula for the variance of the predictor error the usual regression formula for the estimated variance of a predictor error and its generalization to s period prediction is shown to provide a consistent estimator of the mean squared error of the least squares predictor for both stationary and non stationary processes

h_ infty filtering for discrete time switched systems with known sojourn probabilities this technical note deals with the design of mode dependent h infty filters for a class of discrete time switched systems with nonlinearities in this class of systems when the system mode changes the filter designed for the specific subsystem also switches accordingly the main contribution is on the use of the information of the sojourn probability the probability of the switched system staying in each subsystem to build new kind of switched system model when this additional information is available sojourn probabilities are easier to obtain than the transition probabilities commonly used in markovian jump systems applying the lyapunov functional method the bounded real lemma brl for the resulting filtering error system is obtained in theorem 1 the filter parameters are designed in theorem 2 by solving a set of linear matrix inequalities finally two illustrative examples are given to demonstrate the effectiveness and potential of the proposed approach

prediction tests for structural stability abstract three groups of test statistics for testing structural stability are compared the statistics are based on predictions from univariate time series models the first group ignores the specification and estimation variability the second group ignores only the specification uncertainty while the statistics in the third group take into account both specification and estimation variability although corresponding statistics in the three groups have identical asymptotic distributions their small sample distributions differ markedly it is suggested to use the statistics in the third group in practice

control of markovian jump discrete time systems with norm bounded uncertainty and unknown delay this paper studies the problem of control for discrete time delay linear systems with markovian jump parameters the system under consideration is subjected to both time varying norm bounded parameter uncertainty and unknown time delay in the state and markovian jump parameters in all system matrices we address the problem of robust state feedback control in which both robust stochastic stability and a prescribed h sub spl infin performance are required to be achieved irrespective of the uncertainty and time delay it is shown that the above problem can be solved if a set of coupled linear matrix inequalities has a solution

state feedback ℋ control for stochastic time delay nonlinear systems with state and disturbance dependent noise this article focuses on the state feedback control problem for the stochastic nonlinear systems with state and disturbance dependent noise and time varying state delays based on the maxmin optimisation approach both the delay independent and the delay dependent hamilton jacobi inequalities hjis are developed for synthesising the state feedback controller for a general type of stochastic nonlinear systems it is shown that the resulting control system achieves stochastic stability in probability and the prescribed disturbance attenuation level for a class of stochastic affine nonlinear systems the delay independent as well as delay dependent matrix valued inequalities are proposed the resulting control system satisfies global asymptotic stability in the mean square sense and the required disturbance attenuation level by modelling the nonlinearities as uncertainties in corresponding stochastic time delay systems the sufficient conditions in terms of a linear matrix inequality lmi and a bili

effects of not knowing the order of an autoregressive process on the mean squared error of prediction i abstract given a realization of t consecutive observations of an autoregressive process x of unknown order m a process of an arbitrary order p is assumed to be fitted for predicting the future values of another process xtilde t where xtilde t is independent of xt but otherwise has identical probabilistic structure the mean squared error of prediction when the autoregressive coefficients are estimated by least squares is obtained to terms of order t 1 the bias in estimating the autoregressive coefficients is examined the results are illustrated for three processes with m 2 by considering fitting of a process with p 1

analysis of performability for stochastic models of fault tolerant systems performability a composite measure for the performance and reliability may be interpreted as the probability density function of the aggregate reward obtained from a system during its mission time for large mission times we show that known limit theorems lead to an asymptotic normal distribution for the aggregate reward for finite mission times and markovian models we obtain the expressions for all moments of performability and give recursions to compute coefficients involved in the expressions we illustrate the use of the results through an example of a multiple processor computer system

on energy to peak filtering for nonuniformly sampled nonlinear systems a markovian jump system approach this paper focuses on the filter design for nonuniformly sampled nonlinear systems which can be approximated by takagi sugeno t s fuzzy systems the sampling periods of the measurements are time varying and the nonuniform observations of the outputs are modeled by a homogenous markov chain a mode dependent estimator with a fast sampling frequency is proposed such that the estimation can track the signal to be estimated with the nonuniformly sampled outputs the nonlinear systems are discretized with the fast sampling period by using an augmentation technique the corresponding stochastic estimation error system is obtained by studying the stochastic stability and the energy to peak performance of the estimation error system we derive the linear matrix inequality based sufficient conditions the parameters of the mode dependent estimator can be calculated by using the proposed iterative algorithm two examples are used to demonstrate the design procedure and the efficacy of the proposed design method

a multi innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems this paper proposes a generalized extended stochastic gradient gesg algorithm for estimating the parameters of a class of wiener nonlinear autoregressive moving average systems using the gradient search in order to improve the convergence rates of the gesg algorithm a multi innovation gesg algorithm is derived the simulation results indicate that the proposed algorithms can effectively estimate the parameters of a class of output nonlinear systems

fitting time series models by minimizing multistep ahead errors a frequency domain approach this paper brings together two topics in the estimation of time series forecasting models the use of the multistep ahead error sum of squares as a criterion to be minimized and frequency domain methods for carrying out this minimization the methods are developed for the wide class of time series models having a spectrum which is linear in unknown coefficients this includes the ima 1 1 model for which the common exponentially weigh ted moving average predictor is optimal besides more general structural models for series exhibiting trends and seasonality the method is extended to include the box jenkins air line model the value of the multistep criterion is that it provides protection against using an incorrectly specified model the value of frequency domain estimation is that the iteratively reweighted least squares scheme for fitting generalized linear models is readily extended to construct the parameter estimates and their standard errors it also yields insight into the loss of efficiency when the model is correct and the robustness of the criterion against an incorrect model a simple example is used to illustrate the method and a real example demonstrates the extension to seasonal models the discussion considers a diagnostic test statistic for indicating an incorrect model

robust extended dissipative control for sampled data markov jump systems this paper investigates the problem of the sampled data extended dissipative control for uncertain markov jump systems the systems considered are transformed into markov jump systems with polytopic uncertainties and sawtooth delays by using an input delay approach the focus is on the design of a mode independent sampled data controller such that the resulting closed loop system is mean square exponentially stable with a given decay rate and extended dissipative a novel exponential stability criterion and an extended dissipativty condition are established by proposing a new integral inequality the reduced conservatism of the criteria is demonstrated by two numerical examples furthermore a sufficient condition for the existence of a desired mode independent sampled data controller is obtained by solving a convex optimisation problem finally a resistance inductance and capacitance rlc series circuit is employed to illustrate the effectiveness of the proposed approach

a shrinkage estimator for spectral densities summary we propose a shrinkage estimator for spectral densities based on a multilevel normal hierarchical model the first level captures the sampling variability via a likelihood con structed using the asymptotic properties of the periodogram at the second level the spectral density is shrunk towards a parametric time series model to avoid selecting a particular parametric model for the second level a third level is added which induces an estimator that averages over a class of parsimonious time series models the estimator derived from this model the model averaged shrinkage estimator is consistent is shown to be highly competitive with other spectral density estimators via simulations and is computationally inexpensive

output feedback stabilization of networked control systems with a logic zero order hold this paper is concerned with output feedback stabilization of a networked control system in the discrete time domain first a logical zero order hold is introduced to select the newest data packets to drive the plant with which network induced delays and data packet dropouts can be expressed explicitly second an auxiliary vector valued function is introduced to establish a novel finite sum inequality which can provide a tighter bound than some existing ones for some finite sum term third this finite sum inequality is employed to formulate a stability criterion for the closed loop system under consideration it is proven that the stability criterion is of less conservatism than some existing ones reported in the literature fourth a cone complementary linearization based algorithm is presented to design suitable output feedback controllers finally three examples are given to show the effectiveness of the proposed method

exponential stability analysis for delayed semi markovian recurrent neural networks a homogeneous polynomial approach this paper investigates the exponential stability analysis issue for a class of delayed recurrent neural networks rnns with semi markovian parameters by constructing a stochastic lyapunov functional and using some zoom techniques to estimate its weak infinitesimal operator the exponential mean square stability criteria have been proposed for the markovian neural networks with certain transition probabilities we then generalize the homogeneous polynomial approach for the delayed markovian rnns with uncertain transition probabilities during the stability analysis theoretical results have obtained by introducing an appropriate technique for dealing with a large number of complex homogeneous polynomial matrix inequalities finally numerical examples are provided to demonstrate the effectiveness of the proposed technique

brief paper robust h filtering for nonlinear stochastic systems with uncertainties and markov delays this paper investigates the h filtering problem for a class of general nonlinear stochastic systems considering model uncertainties and random time delays governed by markov chains simultaneously the general algebraic hamilton jacobi inequality hji is established first for general nonlinear stochastic systems with uncertainties and random markovian delays then a set of sufficient conditions are derived for the filtering system to achieve stochastic stability in the sense of mean square and the prescribed disturbance attenuation level moreover the developed results are specialized for a special class of nonlinear stochastic systems by presenting the sufficient conditions in terms of matrix inequalities simulation results demonstrate the effectiveness of the proposed approach

h model reduction of markovian jump linear systems abstract in this paper the h model reduction problem for linear systems that possess randomly jumping parameters is studied the development includes both the continuous and discrete cases it is shown that the reduced order models exist if a set of matrix inequalities is feasible an effective iterative algorithm involving linear matrix inequalities is suggested to solve the matrix inequalities characterizing the model reduction solutions using the numerical solutions of the matrix inequalities the reduced order models can be obtained an example is given to illustrate the proposed model reduction method

on bilinear forms in gaussian random variables and toeplitz matrices we improve a result of szeg on the asymptotic behaviour of the trace of products of toeplitz matrices

a novel approach to output feedback control of fuzzy stochastic systems this paper investigates the problem of hankel norm output feedback controller design for a class of t s fuzzy stochastic systems the full order output feedback controller design technique with the hankel norm performance is proposed by the fuzzy basis dependent lyapunov function approach and the conversion on the hankel norm controller parameters sufficient conditions are established to design the controllers such that the resulting closed loop system is stochastically stable and satisfies a prescribed performance the desired output feedback controller can be obtained by solving a convex optimization problem which can be efficiently solved by standard numerical algorithms finally a henon map system is used to illustrate the effectiveness of the proposed techniques

minimum contrast estimation for spectral densities of stationary processes on obtient une large classe d estimateur efficaces par une methode qui est essentiellement differente du maximum de vraisemblance

controllability stabilizability and continuous time markovian jump linear quadratic control consideration is given to the control of continuous time linear systems that possess randomly jumping parameters which can be described by finite state markov processes the relationship between appropriately defined controllability stabilizability properties and the solution of the infinite time jump linear quadratic jlq optimal control problems is also examined although the solution of the continuous time markov jlq problem with finite or infinite time horizons is known only sufficient conditions for the existence of finite cost constant stabilizing controls for the infinite time problem appear in the literature in this paper necessary and sufficient conditions are established these conditions are based on new definitions of controllability observability stabilizability and detectability that are appropriate for continuous time markovian jump linear systems these definitions play the same role for the jlq problem as the deterministic properties do for the linear quadratic regulator lqr problem

a new method for stabilization of networked control systems with random delays we consider the stabilization problem for a class of networked control systems in the discrete time domain with random delays the sensor to controller and controller to actuator delays are modeled as two markov chains and the resulting closed loop systems are jump linear systems with two modes the necessary and sufficient conditions on the existence of stabilizing controllers are established it is shown that state feedback gains are mode dependent an iterative linear matrix inequality lmi approach is employed to calculate the state feedback gains

asymptotically efficient nonparametric estimation of functionals of a spectral density function on considere des observations x 1 x n issues d une serie temporelle gaussienne stationnaire de densite spectrale f

modeling of time series arrays by multistep prediction or likelihood methods abstract an estimation theory is provided for the fitting of possibly incorrect invertible short memory models to short or long memory time series or time series arrays by multistep prediction error minimization or gaussian likelihood maximization by array we mean data y t t 1 t t that depend on the number of observations t such as regression or other estimated model residuals or the outputs of time varying filters for example seasonal adjustments our theory only requires the modeled array to have basic properties for a s i p convergence of parameter estimates the array s sample lagged second moments must converge a s i p and its end values y 1 j t and y t j t must be of order less than t 1 2 or an appropriately differenced version of the observed array must have these properties in findley et al ann statist 29 2001 815 broad classes of arrays were shown to have these properties even for the special case of autoregressive moving average models fit to stationary gaussian time series data our result on the convergence of parameter estimates minimizing p step ahead prediction error sums of squares is new

robust h2 control for discrete time markovian jump linear systems this paper deals with the robust h2 control of discrete time markovian jump linear systems it is assumed that both the state and jump variables are available to the controller uncertainties satisfying some norm bounded conditions are considered on the parameters of the system an upper bound for the h2 control problem is derived in terms of a linear matrix inequality lmi optimization problem for the case in which there are no uncertainties we show that the convex formulation is equivalent to the existence of the mean square stabilizing solution for the set of coupled algebraic riccati equations arising on the quadratic optimal control problem of discrete time markovian jump linear systems therefore for the case with no uncertainties the convex formulation considered in this paper imposes no extra conditions than those in the usual dynamic programming approach finally some numerical examples are presented to illustrate the technique

h sub spl infin control for networked systems with random communication delays this note is concerned with a new controller design problem for networked systems with random communication delays two kinds of random delays are simultaneously considered i from the controller to the plant and ii from the sensor to the controller via a limited bandwidth communication channel the random delays are modeled as a linear function of the stochastic variable satisfying bernoulli random binary distribution the observer based controller is designed to exponentially stabilize the networked system in the sense of mean square and also achieve the prescribed h sub spl infin disturbance attenuation level the addressed controller design problem is transformed to an auxiliary convex optimization problem which can be solved by a linear matrix inequality lmi approach an illustrative example is provided to show the applicability of the proposed method

state estimation and sliding mode control for semi markovian jump systems with mismatched uncertainties this paper is concerned with the state estimation and sliding mode control problems for phase type semi markovian jump systems using a supplementary variable technique and a plant transformation a finite phase type semi markov process has been transformed into a finite markov chain which is called its associated markov chain as a result phase type semi markovian jump systems can be equivalently expressed as its associated markovian jump systems a sliding surface is then constructed and a sliding mode controller is synthesized to ensure that the associated markovian jump systems satisfy the reaching condition moreover an observer based sliding mode control problem is investigated sufficient conditions are established for the solvability of the desired observer two numerical examples are presented to show the effectiveness of the proposed design techniques

a lyapunov proof of an improved maximum allowable transfer interval for networked control systems simple lyapunov proofs are given for an improved relative to previous results that have appeared in the literature bound on the maximum allowable transfer interval to guarantee global asymptotic or exponential stability in networked control systems and also for semiglobal practical asymptotic stability with respect to the length of the maximum allowable transfer interval

small sample effects in time series analysis a new asymptotic theory and a new estimate on introduit un modele mathematique qui couvre les effets des petits echantillons dans l approximation de whittle 1953 on montre que l estimation du maximum de vraisemblance exacte est toujours optimale dans ce modele

lq optimal sampled data control under stochastic delays gridding approach for stabilizability and detectability we solve a linear quadratic optimal control problem for sampled data systems with stochastic delays the delays are stochastically determined by the last few delays the proposed optimal controller

analysis and synthesis of randomly switched systems with known sojourn probabilities in this paper a new approach is proposed and investigated for the stability analysis and stabilizing controller design of randomly switched linear discrete systems the approach is based on sojourn probabilities and it is assumed that these probabilities are known a prior a new lyapunov functional is constructed and two main theorems are proved in this paper theorem 1 gives a sufficient condition for a switched system with known sojourn probabilities to be mean square stable theorem 2 gives a sufficient condition for the design of a stabilizing controller the applications of these theorems and the corresponding corollary and lemma are demonstrated by three numerical examples finally some future research is proposed

a novel markov chain based ilc analysis for linear stochastic systems under general data dropouts environments this technical note contributes to the convergence analysis for iterative learning control ilc for linear stochastic systems under general data dropout environments i e data dropouts occur randomly at both the measurement and actuator sides data updating in the memory array is arranged in such a way that data at every time instance is updated independently which allows successive data dropouts both in time and iteration axes the update mechanisms for both the computed input and real input are proposed and then the update process of both inputs is shown to be a markov chain by virtue of markov modeling a new analysis method is developed to prove the convergence in both mean square and almost sure senses an illustrative example verifies the theoretical results

convex models mle and misspecification we analyze the asymptotic behavior of maximum likelihood estimators mle in convex dominated models when the true distribution generating the independent data does not necessarily belong to the model inspired by the hellinger distance and its properties we introduce a family of divergences contrast functions which allow a unified treatment of well and misspecified convex models convergence and rates of convergence of the mle with respect to our divergences are obtained from inequalities satisfied by these divergences and results from empirical process theory uniform laws of large numbers and maximal inequalities as a particular case we recover existing results for hellinger convergence of mle in well specified convex models four examples are considered mixtures of discrete distributions monotone densities decreasing failure rate distributions and a finite dimensional parametric model

stabilization of singular markovian jump systems with time varying switchings this paper studies the stabilization problem for a class of singular markovian jump systems smjss with time varying transition rates by using the quantized method firstly by proposing a quantization principle the time varying transition rate matrix trm is quantized into a series of finite trms with norm bounded uncertainties then the difficulties of the time varying trm confronted in system analysis and synthesis can be overcome new criteria for stochastic admissibility analysis as well as controller synthesis are established in terms of linear matrix inequalities lmis finally the validity of the theoretical results is shown by numerical examples