on stabilization of bilinear uncertain time delay stochastic systems with markovian jumping parameters in this paper we investigate the stochastic stabilization problem for a class of bilinear continuous time delay uncertain systems with markovian jumping parameters specifically the stochastic bilinear jump system under study involves unknown state time delay parameter uncertainties and unknown nonlinear deterministic disturbances the jumping parameters considered here form a continuous time discrete state homogeneous markov process the whole system may be regarded as a stochastic bilinear hybrid system that includes both time evolving and event driven mechanisms our attention is focused on the design of a robust state feedback controller such that for all admissible uncertainties as well as nonlinear disturbances the closed loop system is stochastically exponentially stable in the mean square independent of the time delay sufficient conditions are established to guarantee the existence of desired robust controllers which are given in terms of the solutions to a set of either linear matrix inequalities lmis or coupled quadratic matrix inequalities the developed theory is illustrated by numerical simulation

adaptive control of linear systems with markov perturbations the stochastic model considered is a linear jump diffusion process x for which the coefficients and the jump processes depend on a markov chain z with finite state space first we study the optimal filtering and control problem for these systems with non gaussian initial conditions given noisy observations of the state x and perfect measurements of z we derive a new sufficient condition which ensures the existence and the uniqueness of the solution of the nonlinear stochastic differential equations satisfied by the output of the filter we study a quadratic control problem and show that the separation principle holds next we investigate an adaptive control problem for a state process x defined by a linear diffusion for which the coefficients depend on a markov chain the processes x and z being observed in independent white noises suboptimal estimates for the process x z and approximate control law are investigated for a large class of probability distributions of the initial state asymptotic properties of these filters and this control law are obtained upper bounds for the corresponding error are given

on independence and conditioning on wiener space notant i f et i g des integrales de wiener ito multiples d ordre p et q respectivement on donne une condition necessaire et suffisante sur le couple f g sous laquelle les variables aleatoires i f et i g sont independantes ceci est base sur une caracterisation generale de l independance de variables aleatoires dans l espace de wiener dans le contexte d un calcul variationnel stochastique

robust h sup spl infin filtering for uncertain markovian jump systems with mode dependent time delays this paper considers the problem of robust h sup spl infin filtering for uncertain markovian jump linear systems with time delays which are time varying and depend on the system mode the parameter uncertainties are time varying norm bounded the aim of this problem is to design a markovian jump linear filter that ensures robust exponential mean square stability of the filtering error system and a prescribed l sub 2 induced gain from the noise signals to the estimation error for all admissible uncertainties a sufficient condition for the solvability of this problem is obtained the desired filter can be constructed by solving a set of linear matrix inequalities an illustrative numerical example is provided to demonstrate the effectiveness of the proposed approach

the wonham filter with random parameters rate of convergence and error bounds let spl alpha t be a finite state continuous time markov chain with generator q q sup ij spl isin r sup m spl times m and state space m z sup i z sup m where z sup 1 for i sub spl middot spl middot spl middot spl middot m are distinct real numbers when the state space and the generator are known a priori the best estimator of spl alpha t in terms of mean square error under noisy observation is the classical wonham filter this note addresses the estimation issue when values of the state space or values of the generator are unknown a priori in each case we propose a suboptimal filter and prove its convergence to the desired wonham filter under simple conditions moreover we obtain the rate of convergence using both the mean square and the higher moment error bounds

adaptive sampled data based linear quadratic optimal control of stochastic systems the problem of sampled data sd based adaptive linear quadratic lq optimal control is considered for linear stochastic continuous time systems with unknown parameters and disturbances to overcome the difficulties caused by the unknown parameters and incompleteness of the state information and to probe into the influence of sample size on system performance a cost biased parameter estimator and an adaptive control design method are presented under the assumption that the unknown parameter belongs to a known finite set some sufficient conditions ensuring the convergence of the parameter estimate are obtained it is shown that when the sample step size is small the sd based adaptive control is lq optimal for the corresponding discretized system and sub optimal compared with that of the case where the parameter is known and the information is complete

embedding the abstract wiener space in a probability space abstract in the first part of this paper it is pointed out that for certain applications of the stochastic calculus of variations it is useful to replace the classical domain of definition the wiener space with a general probability space in which the wiener space is embedded this yields a certain conditional malliavin calculus and is applicable to signal and noise problems in a somewhat analogous way it is pointed out in the second part of the paper that formulating the ito calculus in a setup of an abstract wiener space embedded in a general probability space endowed with a filtration has certain useful applications in particular it enables the formulation and derivation of a dimension free form of the girsanov theorem as well as a dimension free form of the representation of l p wiener functionals as ito integrals

optimality analysis of adaptive sampled control of hybrid systems with quadratic index this note is concerned with the sampled data based linear quadratic lq adaptive control of continuous time systems with unknown markov jump parameters a parameter estimator and a control design method are given it is shown that when the sample step size is small the sampled data based adaptive control is suboptimal under lq index the result is illustrated by a simulation example

combined 5 2 cv f test for comparing supervised classification learning algorithms dietterich 1998 reviews five statistical tests and proposes the 5 2 cv t test for determining whether there is a significant difference between the error rates of two classifiers in our experiments we noticed that the 5 2 cv t test result may vary depending on factors that should not affect the test and we propose a variant the combined 5 2 cv f test that combines multiple statistics to get a more robust test simulation results show that this combined version of the test has lower type i error and higher power than 5 2 cv proper

a crame spl acute r rao type estimation lower bound for systems with measurement faults a crame spl acute r rao type lower bound is presented for systems with measurements prone to discretely distributed faults which are a class of hybrid systems lower bounds for both the state and the markovian interruption variables fault indicators of the system are derived using the recently presented sequential version of the crame spl acute r rao lower bound crlb for general nonlinear systems because of the hybrid nature of the systems addressed the crlb cannot be directly applied due to violation of its associated regularity conditions to facilitate the calculation of the lower bound the hybrid system is first approximated by a system in which the discrete distribution of the fault indicators is replaced by an approximating continuous one the lower bound is then obtained via a limiting process applied to the approximating system the results presented herein facilitate a relatively simple calculation of a nontrivial lower bound for the state vector of systems with fault prone measurements the crlb type lower bound for the interruption process variables turns out to be trivially zero however a nontrivial non crlb type bound for these variables has been recently presented elsewhere by the authors the utility and applicability of the proposed lower bound are demonstrated via a numerical example involving a simple global positioning system gps aided navigation system where the gps measurements are fault prone due to their sensitivity to multipath errors

absolutely continuous flows generated by sobolev class vector fields in finite and infinite dimensions abstract we prove the existence of the global flow ut generated by a vector field a from a sobolev class w1 1 on a finite or infinite dimensional space x with a measure provided is sufficiently smooth and that a a and a where a is the divergence with respect to are exponentially integrable in addition the measure is shown to be quasi invariant under ut in the case x r n and p dx where p w1 1loc r n is a locally uniformly positive probability density a sufficient condition is exp c a exp c a p p l1 for all c in the infinite dimensional case we get analogous results for measures differentiable along sufficiently many directions examples of measures which fit our framework important for applications are symmetric invariant measures of infinite dimensional diffusions and gibbs measures typically in both cases such measures are essentially non gaussian our result in infinite dimensions significantly extends previously studied cases where was a gaussian measure finally we study flows generated by vector fields whose values are not necessarily in the cameron martin space

generalized stochastic integrals and the malliavin calculus the paper first reviews the skorohod generalized stochastic integral with respect to the wiener process over some general parameter space t and it s relation to the malliavin calculus as the adjoint of the malliavin derivative some new results are derived and it is shown that every sufficiently smooth process ut t t can be decomposed into the sum of a malliavin derivative of a wiener functional and a process whose generalized integral over t vanishes using the results on the generalized integral the bismut approach to the malliavin calculus is generalized by allowing non adapted variations of the wiener process yielding sufficient conditions for the existence of a density which is considerably weaker than the previously known conditions

mean square stochastic stability of linear time delay system with markovian jumping parameters this paper deals with the mean square stochastic stability of the class of linear time delay systems with markovian jumping parameters a delay independent sufficient condition for checking the stochastic stability of this class of systems is established a numerical example is given to show the usefulness of the proposed theoretical results

geometric analysis of conditional independence on wiener space in this paper we study the conditional independence of sigma fields on the wiener space using the tools of the stochastic calculus of variations particular emphasis is given to the relation between the splitting of the random tangent spaces associated to the sigma fields and the conditional independence

stability analysis of switched stochastic systems in this paper for a class of switched stochastic ss systems the moment stability m s and sample path stability sp s are investigated respectively and there are two main contributions first based on accurate estimations for the powers of solution of a special nonswitched stochastic nss system by employing the concepts of a lyapunov function and describing the switching laws with the average dwell time and the subsystems three sufficiency theorems of p th m s are given for the ss systems then for the sp s of such systems based on the results of p th m s two sufficiency theorems are obtained for p 2 and p 2 respectively

quadratic control with partial information for discrete time jump systems with the markov chain in a general borel space this paper deals with the finite horizon quadratic optimal control problem of discrete time markov jump linear systems mjls considering the case in which the markov chain takes values in a general borel space s it is assumed that the controller has access to an output variable as well as the jump parameter the goal is to design a dynamic markov jump controller such that the closed loop system minimizes the quadratic functional cost of the system over a finite horizon period of time it is shown that an optimal controller can be obtained from two s coupled difference riccati equations one associated to a filtering problem and the other one associated to a control problem in which the state variable is fully available by s coupled it is meant that the difference riccati equations are coupled via an integral over s this result which can be seen as a separation principle for discrete time mjls generalizes previous ones that were restricted to the markov chain taking values in a finite set

a theoretical analysis of bagging as a linear combination of classifiers we apply an analytical framework for the analysis of linearly combined classifiers to ensembles generated by bagging this provides an analytical model of bagging misclassification probability as a function of the ensemble size which is a novel result in the literature experimental results on real data sets confirm the theoretical predictions this allows us to derive a novel and theoretically grounded guideline for choosing bagging ensemble size furthermore our results are consistent with explanations of bagging in terms of classifier instability and variance reduction support the optimality of the simple average over the weighted average combining rule for ensembles generated by bagging and apply to other randomization based methods for constructing classifier ensembles although our results do not allow to compare bagging misclassification probability with the one of an individual classifier trained on the original training set we discuss how the considered theoretical framework could be exploited to this aim

representation of the distributions on wiener space and stochastic calculus of variations abstract in this work we construct projections and dual projections with respect to the past of the wiener process for the cameron martin space valued distributions on the wiener space with their help we then prove on the one hand an extension of clark s formula to the representation of the scalar distributions with the extended ito integral and on the other hand we give a decomposition of the vector valued distributions by exactly calculating the kernel of the divergence operator

on controlability of linear systems with stochastic jump parameters this note studies the notion of stochastic e controllability in probability for linear systems subject to sudden jumps in parameter values by using a lyapunov like approach sufficient conditions are obtained in terms of a set of coupled bernoulli equations the potential use of the results is illustrated by an example

mean field stochastic linear quadratic optimal control with markov jump parameters abstract this paper considers a class of mean field stochastic linear quadratic optimal control problems with markov jump parameters the new feature of these problems is that means of state and control are incorporated into the systems and the cost functional based on the modes of markov chain the corresponding decomposition technique of augmented state and control is introduced it is shown that under some appropriate conditions there exists a unique optimal control which can be explicitly given via solutions of two generalized difference riccati equations a numerical example sheds light on the theoretical results established

stochastic stability properties of jump linear systems jump linear systems are defined as a family of linear systems with randomly jumping parameters usually governed by a markov jump process and are used to model systems subject to failures or changes in structure the authors study stochastic stability properties in jump linear systems and the relationship among various moment and sample path stability properties it is shown that all second moment stability properties are equivalent and are sufficient for almost sure sample path stability and a testable necessary and sufficient condition for second moment stability is derived the lyapunov exponent method for the study of almost sure sample stability is discussed and a theorem which characterizes the lyapunov exponents of jump linear systems is presented finally for one dimensional jump linear system it is proved that the region for delta moment stability is monotonically converging to the region for almost sure stability at delta down arrow 0 sup

efficient estimation of first passage time density function for jump diffusion processes the first passage time problem has attracted considerable research interest in the field of stochastic processes it concerns the estimation of the probability density of the time for a random process to cross a specified boundary level even though there are many theoretical advances in solving this problem for many classes of random processes no analytical solution exists the jump diffusion process jdp is one such class recent research in finance theory has renewed the interest in jdps and the first passage time problem for such processes is applicable to several finance problems in this paper we develop fast monte carlo type numerical methods for computing the first passage density function for jdps compared with the standard monte carlo approach our approaches are about 10 30 times faster

random ordinality ensembles data with multi valued categorical attributes can cause major problems for decision trees the high branching factor can lead to data fragmentation where decisions have little or no statistical support in this paper we propose a new ensemble method random ordinality ensembles roe that reduces this problem and provides significantly improved accuracies over current ensemble methods we perform a random projection of the categorical data into a continuous space as the transformation to continuous data is a random process each dataset has a different imposed ordinality a decision tree that learns on this new continuous space is able to use binary splits hence reduces the data fragmentation problem generally these binary trees are accurate diverse training datasets ensure diverse decision trees in the ensemble we created two variants of the technique roe in the first variant we used decision trees as the base models for ensembles in the second variant we combined the attribute randomisation of random subspaces with random ordinality these methods match or outperform other popular ensemble methods different properties of these ensembles were studied the study suggests that random ordinality trees are generally more accurate and smaller than multi way split decision trees it is also shown that random ordinality attributes can be used to improve bagging and adaboost m1 ensemble methods

stabilizing control law for hybrid models this note is concerned with the adaptive stabilizing control problem for jump markov systems a new control law is proposed based on a simple set of algebraic sufficient conditions it is shown that this approach can be used for a class of stochastic models for which the more restrictive solution proposed by caines and zhang 1992 does not apply

on the structure of independence on wiener space motives par des resultats de travaux anterieurs sur la caracterisation de l independance de variables aleatoires dans l espace de wiener les auteurs etudient ici la relation entre l independance de certaines classes de sous algebres et l orthogonalite des h derivees faibles des variables aleatoires qui engendrent ces sous algebres ou c h est l espace de wiener classique

robust stability and controllability of stochastic differential delay equations with markovian switching in this paper we investigate the almost surely asymptotic stability for the nonlinear stochastic differential delay equations with markovian switching some sufficient criteria on the controllability and robust stability are also established for linear stochastic differential delay equations with markovian switching

delay dependent robust stabilization of uncertain state delayed systems this paper concerns a problem of robust stabilization of uncertain state delayed systems a new delay dependent stabilization condition using a memoryless controller is formulated in terms of matrix inequalities an algorithm involving convex optimization is proposed to design a controller guaranteeing a suboptimal maximal delay such that the system can be stabilized for all admissible uncertainties

explicit controller formulas for lmi based h synthesis the set of h controllers with closed loop performance c can be implicitly parametrized by the solutions r s of a system of linear matrix inequalities lmi the matrices r and s play a role analogous to that of the riccati solutions x and y in classical riccati based h control useful applications include lmi based h synthesis mixed h 2h design and h design with a pole placement constraint this paper is concerned with the reliable computation of h controllers given a solution r s of the characteristic system of lmis explicit formulas are derived for both the regular and singular cases remarkably these formulas are extensions of the usual central controller formulas where the lmi solutions r and s replace the riccati solutions x and y simple and numerically appealing new formulas for discrete time h controllers are also derived

stability analysis of a class of hybrid stochastic retarded systems under asynchronous switching the stability of a class of hybrid stochastic retarded systems hsrss with an asynchronous switching controller is investigated in this model the controller design relies on the observed jumping parameters which are however delayed and thus can not be measured in real time precisely this delayed time interval referred to as the asynchronous switching interval is markovian and dependent on the actual switching signal the sufficient conditions under which the system is either stochastically asymptotic stable or input to state stable are obtained by applying the extended razumikhin type theorem to the asynchronous switching interval these results are less conservative as it is only required that the designed lyapunov function is non decreasing it is shown that the stability of the considered system can be guaranteed by a sufficiently small mode transition rate of the underlying markov process which is a conclusion similar to that in asynchronous deterministic switched systems the effectiveness and correctness of the obtained results are finally verified by a numerical example

optimal adaptive lqg control for systems with finite state process parameters the situation where a totally observed process y t is generated by a stochastic differential equation whose parameters evolve on a finite set 1 n according to a stochastic differential equation is considered the optimal control law is sought with respect to quadratic loss functions on y t and the control u t the auxiliary p d e technique of hijab 6 is used together with a nonlinear filter to obtain the solution whose existence depends upon that of a smooth solution to the auxiliary p d e and strong solutions to the system s d e under the given control inputs

a delay dependent stability criterion for systems with uncertain time invariant delays this paper provides a new stability criterion for systems with time invariant uncertain delays based on an improved upper bound for the inner product of two vectors a new delay dependent robust stability criterion is derived which is shown by an example less conservative than existing stability criteria

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

almost sure stabilization for feedback controls of regime switching linear systems with a hidden markov chain this work is devoted to the almost sure stabilization of adaptive control systems that involve an unknown markov chain the control system displays continuous dynamics represented by differential equations and discrete events given by a hidden markov chain in the previous investigation on this class of problems averaging criteria were used which provides only the system behavior in some expectation sense a closer scrutiny of the system behavior necessarily requires the consideration of sample path properties different from previous work on stabilization of adaptive controlled systems with a hidden markov chain where average criteria were considered this work focuses on the almost sure stabilization or sample path stabilization of the underlying processes under simple conditions it is shown that as long as the feedback controls have linear growth in the continuous component the resulting process is regular moreover by appropriate choice of the lyapunov functions it is shown that the adaptive system is stabilizable almost surely as a by product it is also established that the controlled process is positive recurrent

a note on continuous time els abstract the parameter estimation of continuous time finite dimensional linear stochastic systems is a problem of long standing interest the method usually used is the extended least squares els algorithm described by a nonlinear stochastic differential equation sld with the existence of the global strong solution assumed this paper shows that the els estimate does exist in 0 and at the same time presents a number of convergence results paralleling those for the discrete time case

on the adaptive control for jump parameter systems vianonlinear filtering in this paper we first present an error analysis for the process of estimates generated by the wonham filter when it is used for the estimation of the finite set valued jump markov parameters of a random parameter linear stochastic system and further give bounds on certain functions of these estimates we then consider a certainty equivalence adaptive linear quadratic gaussian feedback control law using the estimates generated by the nonlinear filter and demonstrate the global existence of solutions to the resulting closed loop system a stochastic lyapunov analysis establishes that the certainty equivalence law stabilizes the markov jump parameter linear system in the mean square average sense the conditions for this result are that certain products of i the parameter process jump rate and ii the solution of the control riccati equation and its second derivatives should be less than certain given bounds an example is given where the controlled linear system has state dimension 2 finally the stabilizing properties of certainty equivalence laws which depend on i the maximum likelihood estimate of the parameter value and ii a modified version of this estimate are established under certain conditions

tracking and identification of regime switching systems using binary sensors this work is concerned with tracking and system identification for time varying parameters the parameters are markov chains and the observations are binary valued with noise corruption to overcome the difficulties due to the limited measurement information wonham type filters are developed first then based on the filters two popular estimators namely mean squares estimator msq and maximum posterior map estimator are constructed for the mean squares estimator we derive asymptotic normality in the sense of weak convergence and in the sense of strong approximation the asymptotic normality is then used to derive error bounds when the markov chain is infrequently switching we derive error bounds for map estimators when the markovian parameters are fast varying we show that the averaged behavior of the parameter process can be derived from the stationary measure of the markov chain and that can be estimated using empirical measures upper and lower error bounds on estimation errors are also established

stability in distribution of stochastic differential delay equations with markovian switching in this paper we discuss stochastic differential delay equations with markovian switching such an equation can be regarded as the result of several stochastic differential delay equations switching from one to another according to the movement of a markov chain the aim of this paper is to investigate the stability in distribution of the equations

robust output feedback h control of uncertain markovian jump systems with mode dependent time delays this paper describes the synthesis of robust output feedback h control for a class of uncertain markovian jump linear systems with time delays which are time varying and depend on the system modes of operation under the assumption of known bounds of system uncertainties and the control system gain variations we present sufficient conditions on the existence of robust stochastic stability and disturbance h attenuation through the changes of variables and schur complements these sufficient conditions can be rewritten in a set of coupled linear matrix inequalities with which robust control can be easily constructed as an added advantage the control design depends only on the measured parameter of markovian jumping at time t which might be corrupted by measurement noises numerical examples are provided to demonstrate the effectiveness of the proposed approach

some relations among classes of σ fields on wiener space in this work we study sigma fields and their tangent spaces on the wiener space which are invariant in some sense with respect to the basic operators of the malliavin calculus

stabilization of markovian systems via probability rate synthesis and output feedback this technical note is concerned with the stabilization problem of markovian jump linear systems via designing switching probability rate matrices and static output feedback gains a novel necessary and sufficient condition is established to characterize the switching probability rate matrices that guarantee the mean square stability of markovian jump linear systems based on this a necessary and sufficient condition is provided for the existence of desired controller gains and probability rate matrices extensions to the polytopic uncertain case are also provided all the conditions are formulated in terms of linear matrix inequalities with some equality constraints which can be solved by two modified cone complementarity linearization algorithms examples are given to show the effectiveness of the proposed method

output feedback for a class of linear systems with stochastic jump parameters a class of linear systems subject to sudden jumps in parameter values is studied for a quadratic performance index the optimal controller has already been proposed it consists of a full state feedback that requires the synthesis of an observer for the plant state and mode to avoid this difficulty the present note develops new results that lead to the selection of the best feedback control law depending only on measurable outputs necessary optimality conditions are derived and two computationnal algorithms proposed an example is included

control of nonlinear systems with markovian parameter changes the control of hybrid systems in which a totally observed state process is generated by a nonlinear stochastic differential equation whose parameters are functions of a random jump process called the plant regime is considered regime transitions typically represent target maneuvers in a tracking system the control task is split between a jump detection and identification jdi filter and a discrete adaption or reconfiguration a design that takes into account the errors of the jdi device and the plant nonlinearities to produce a near optimal passively adaptive control law with an easily implemented affine structure is presented

a separation principle for the continuous time lq problem with markovian jump parameters in this paper we devise a separation principle for the finite horizon quadratic optimal control problem of continuous time markovian jump linear systems driven by a wiener process and with partial observations we assume that the output variable and the jump parameters are available to the controller it is desired to design a dynamic markovian jump controller such that the closed loop system minimizes the quadratic functional cost of the system over a finite horizon period of time as in the case with no jumps we show that an optimal controller can be obtained from two coupled riccati differential equations one associated to the optimal control problem when the state variable is available and the other one associated to the optimal filtering problem this is a separation principle for the finite horizon quadratic optimal control problem for continuous time markovian jump linear systems for the case in which the matrices are all time invariant we analyze the asymptotic behavior of the solution of the derived interconnected riccati differential equations to the solution of the associated set of coupled algebraic riccati equations as well as the mean square stabilizing property of this limiting solution when there is only one mode of operation our results coincide with the traditional ones for the lqg control of continuous time linear systems

perceptual multi channel visual feature fusion for scene categorization abstract effectively recognizing sceneries from a variety of categories is an indispensable but challenging technique in computer vision and intelligent systems in this work we propose a novel image kernel based on human gaze shifting aiming at discovering the mechanism of humans perceiving visually semantically salient regions within a scenery more specifically we first design a weakly supervised embedding algorithm which projects the local image features i e graphlets in this work onto the pre defined semantic space thereby we describe each graphlet by multiple visual features at both low level and high level it is generally acknowledged that humans attend to only a few regions within a scenery thus we formulate a sparsity constrained graphlet ranking algorithm which incorporates visual clues at both the low level and the high level according to human visual perception these top ranked graphlets are either visually or semantically salient we sequentially connect them into a path which mimics human gaze shifting lastly a so called gaze shifting kernel gsk is calculated based on the learned paths from a collection of scene images and a kernel svm is employed for calculating the scene categories comprehensive experiments on a series of well known scene image sets shown the competitiveness and robustness of our gsk we also demonstrated the high consistency of the predicted path with real human gaze shifting path

passivity analysis and passification of uncertain markovian jump systems with partially known transition rates and mode dependent interval time varying delays in this paper the problem of delay dependent robust passivity analysis and robust passification of uncertain markovian jump linear systems mjlss with partially known transition rates and mode dependent time varying delays are investigated in the deterministic model the time varying delay is in a given range and the uncertainties are assumed to be norm bounded by constructing an appropriate lyapunov krapunov functional lkf combining with jensen s inequality and the free weighting matrix method delay dependent passification conditions are obtained in term of linear matrix inequalities lmis for the robust passification problem desired passification controllers are designed which guarantee that the closed loop mjls is passive finally a numerical example is given to illustrate the effectiveness of the proposed approach

the random subspace method for constructing decision forests much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes the dilemma between overfitting and achieving maximum accuracy is seldom resolved a method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity the classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector that is trees constructed in randomly chosen subspaces the subspace method is compared to single tree classifiers and other forest construction methods by experiments on publicly available datasets where the method s superiority is demonstrated we also discuss independence between trees in a forest and relate that to the combined classification accuracy

brief non fragile h control for linear systems with multiplicative controller gain variations this paper is concerned with the problem of non fragile h controller design for linear time invariant systems the controller to be designed is assumed to have multiplicative gain variations design methods are presented in terms of symmetric positive definite solutions of algebraic riccati inequalities the resulting design is such that the closed loop system is robustly stable and has an h disturbance attenuation bound with respect to the multiplicative controller gain variations a numerical example is given to illustrate the design methods

robust h sub spl infin control of uncertain markovian jump systems with time delay this paper is concerned with the robust stochastic stabilizability and robust h sub spl infin disturbance attenuation for a class of uncertain linear systems with time delay and randomly jumping parameters the transition of the jumping parameters is governed by a finite state markov process sufficient conditions on the existence of a robust stochastic stabilizing and spl gamma suboptimal h sub spl infin state feedback controller are presented using the lyapunov functional approach it is shown that a robust stochastically stabilizing h sub spl infin state feedback controller can be constructed through the numerical solution of a set of coupled linear matrix inequalities