sliding mode control design based on ackermann s formula the sliding mode control methods are developed to design systems which have the desired dynamic behavior and are robust with respect to perturbations it is shown that the discontinuity plane for sliding mode control may be found in an explicit form using ackermann s formula two design procedures are derived first static controllers are designed to enforce sliding modes with the desired dynamic properties after a finite time interval then dynamic controllers are designed that exhibit the desired dynamic properties during the entire control process

fuzzy output tracking control of semi linear first order hyperbolic pde systems with matched perturbations abstract this paper considers the design problem of a fuzzy output tracking controller via a finite number of actuators for a class of nonlinear distributed parameter systems with unknown matched perturbations described by semi linear first order hyperbolic partial differential equations pdes firstly a t s fuzzy hyperbolic pde model is assumed to be used to accurately represent the semi linear first order hyperbolic pde system subsequently based on the t s fuzzy pde model the classic variable structure control technique is adopted to develop a fuzzy output tracking controller such that the output of the semi linear pde system exponentially tracks a desired reference and the state of the system is uniformly bounded in the filippov sense by constructing a strict lyapunov function the sufficient condition on the existence of the fuzzy output tracking controller is derived and presented in terms of algebraic linear matrix inequalities lmis in space with an equality constraint it has been shown that when these algebraic lmis with an equality constraint are feasible fuzzy tracking control design can be obtained via the existing convex optimization techniques finally the developed design methodology is successfully applied to the control of a semi linear lotka volterra pde system and the achieved results illustrate its effectiveness

adaptive importance sampling monte carlo simulation for general multivariate probability laws we establish a parametric adaptive importance sampling variance reduction method for general multivariate probability laws employing the principle of bypass distributions makes it possible to develop adaptive algorithms without relying on particular properties of the target and proposal laws both of which in the proposed framework are as general as the uniform law on the unit hypercube without changing the sampling distribution at each iteration we establish the asymptotic normality of the estimator of the desired mean and of the importance sampling parameter as the number of observations tends to infinity although implementation of the proposed methodology requires a small amount of initial work it has the potential to yield substantial improvements in estimator efficiency in various general problem settings to illustrate the applicability and effectiveness we provide numerical results throughout in which we apply exponential and normal bypass distributions as well as demonstrate that well known adaptive importance sampling formulations in the literature can be easily rewritten in the proposed framework

sliding mode control of uncertain fractional order systems with delay abstractthis paper presents a sliding mode control smc based framework to design a stabilizing controller for uncertain fractional order time delay systems fotds using the lambert w function te

on the partial realization problem abstract in this paper we take a unified approach to the partial realization problem in which we seek to incorporate ideas from numerical linear algebra most of which were originally developed in other contexts we approach the partial realization problem from several different angles and explore the connections to such topics as factoriza tion of hankel matrices block tridiagonalization generalizations of the lanczos process for biorthogonalization the euclidean algorithm and the principal part con tinued fractions of arne magnus the pade table and the berlekamp masseyalgo rithm in this way we are able to clarify some previous results by rissanen kalman and others and place them in a broader context this leads to several results and concepts which we think are new our analysis is restricted to the scalar case but some definitions and formulations have been rigged to facilitate an extension to the matrix case

sliding mode control design for nonlinear systems using probability density function shaping in this paper we propose a sliding mode based stochastic distribution control algorithm for nonlinear systems where the sliding mode controller is designed to stabilize the stochastic system and stochastic distribution control tries to shape the sliding surface as close as possible to the desired probability density function kullback leibler divergence is introduced to the stochastic distribution control and the parameter of the stochastic distribution controller is updated at each sample interval rather than using a batch mode it is shown that the estimated weight vector will converge to its ideal value and the system will be asymptotically stable under the rank condition which is much weaker than the persistent excitation condition the effectiveness of the proposed algorithm is illustrated by simulation

multiobjective output feedback control via lmi optimization this paper presents an overview of a linear matrix inequality lmi approach to the multiobjective synthesis of linear output feedback controllers the design objectives can be a mix of h sub spl infin performance h sub 2 performance passivity asymptotic disturbance rejection time domain constraints and constraints on the closed loop pole location in addition these objectives can be specified on different channels of the closed loop system when all objectives are formulated in terms of a common lyapunov function controller design amounts to solving a system of linear matrix inequalities the validity of this approach is illustrated by a realistic design example

nonparametric importance sampling abstract importance sampling is a widely used variance reduction simulation technique for the evaluation of high dimensional integrals a key step in the implementation of importance sampling is to choose a proper distribution function from which pseudorandom numbers are generated parametric sampling distributions if available at all are often inadequate for high dimensional integrals over irregular regions one possible remedy is to use a nonparametric method to estimate the unknown optimal sampling function we show that the nonparametric approach yields integral estimates that converge faster than estimates obtained from parametric approaches we also demonstrate that an adaptive method which has been used successfully in parametric settings does not yield better results than simple one step methods in the nonparametric setting

antithetic acceleration of monte carlo integration in bayesian inference abstract it is proposed to sample antithetically rather than randomly from the posterior density in bayesian inference using monte carlo integration conditions are established under which the number of replications required with antithetic sampling relative to the number required with random sampling is inversely proportional to sample size as sample size increases the result is illustrated in an experiment using a bivariate vector autoregression

inference for deterministic simulation models the bayesian melding approach abstract deterministic simulation models are used in many areas of science engineering and policy making typically these are complex models that attempt to capture underlying mechanisms in considerable detail and they have many user specified inputs the inputs are often specified by some form of trial and error approach in which plausible values are postulated the corresponding outputs inspected and the inputs modified until plausible outputs are obtained here we address the issue of more formal inference for such models a probabilistic approach called bayesian synthesis was shown to suffer from the borel paradox according to which the results can depend on the parameterization of the model we propose a modified approach called bayesian melding which takes into full account information and uncertainty about both inputs and outputs to the model while avoiding the borel paradox this is done by recognizing the existence of two priors one implicit and one explicit on each input and output

recent developments in nonparametric density estimation advances in computation and the fast and cheap computational facilities now available to statisticians have had a significant impact upon statistical research and especially the development of nonparametric data analysis procedures in particular theoretical and applied research on nonparametric density estimation has had a noticeable influence on related topics such as nonparametric regression nonparametric discrimination and nonparametric pattern recognition this article reviews recent developments in nonparametric density estimation and includes topics that have been omitted from review articles and books on the subject the early density estimation methods such as the histogram kernel estimators and orthogonal series estimators are still very popular and recent research on them is described different types of restricted maximum likelihood density estimators including order restricted estimators maximum penalized likelihood estimators and sieve estimators are discussed where restrictions are imposed upon the class of densities or on the form of the likelihood function nonparametric density estimators that are data adaptive and lead to locally smoothed estimators are also discussed these include variable partition histograms estimators based on statistically equivalent blocks nearest neighbor estimators variable kernel estimators and adaptive kernel estimators for the multivariate case extensions of methods of univariate density estimation are usually straightforward but can be computationally expensive a method of multivariate density estimation that did not spring from a univariate generalization is described namely projection pursuit density estimation in which both dimensionality reduction and density estimation can be pursued at the same time finally some areas of related research are mentioned such as nonparametric estimation of functionals of a density robust parametric estimation semiparametric models and density estimation for censored and incomplete data directional and spherical data and density estimation for dependent sequences of observations

filtering with state space localized kalman gain abstract it is well known that particle filters and markov chain monte carlo methods are too expensive for high dimensional models more robust techniques such as low rank versions of the kalman filter are usually applied in large scale models common feature of all the ensemble kalman type filters is that the model state and parameters are updated using the covariance matrix between the model states and the measurements often estimated by a finite number of samples in many nonlinear problems a global linearization of the model may be inadequate to describe the relationship between the observations and the model state and parameters hence the kalman type updates may cause severe bias in the posterior estimates in this paper we investigate the effect of using a local linearization motivated by local gaussian density estimation we describe ways to estimate the covariance locally in the state space resulting in local ensemble filters this is not to be confused with covariance localization which often refers to localization in the physical space the filters are tested on the three dimensional lorenz model and a three layer petroleum reservoir type model the results clearly show that using a kalman update based on local covariances allows resolution of small scale structures in the posterior distribution that cannot be resolved using a global covariance matrix in particular we show that the new local filters are able to approximate posterior distributions with multiple modes contrary to the ensemble kalman filter

parametrising the solutions of the generalised partial realisation problem through an atlas of coordinates given a finite nice sequence of matrices and a positive integer d the set of generalised partial realisations of of order d is a stratified smooth manifold an atlas of coordinate charts for each stratum is provided that locally parametrises it in particular the set of all generalised partial realisations of order d of any given nice sequence will be described

minimal rank completions for block matrices abstract this paper deals with the following problem of completion of block matrices let aij i j be given matrices find additional matrices aij such that the completion a aij ni j 1 has lowest possible rank the structure of the set of all minimal rank completions is studied special attention is paid to the toeplitz case together with its connection to the partial realization problem

approximating posterior distributions by mixtures kernel density estimation techniques are used to smooth simulated samples from importance sampling function approximations to posterior distributions resulting in revised approximations that are mixtures of standard parametric forms usually multivariate normal or t distributions adaptive refinement of such mixture approximations involves repeating this process to home in successively on the posterior in fairly low dimensional problems this provides a general and automatic method of approximating posteriors by mixtures so that marginal densities and other summaries may be easily computed

the maximal smoothing principle in density estimation abstract we propose a widely applicable method for choosing the smoothing parameters for nonparametric density estimators it has come to be realized in recent years e g see hall and marron 1987 scott and terrell 1987 that cross validation methods for finding reasonable smoothing parameters from raw data are of very limited practical value their sampling variability is simply too large the alternative discussed here the maximal smoothing principle suggests that we consider using the most smoothing that is consistent with the estimated scale of our data this greatly generalizes and exploits a phenomenon noted in terrell and scott 1985 that measures of scale tend to place upper bounds on the smoothing parameters that minimize asymptotic mean integrated squared error of density estimates such as histograms and frequency polygons the method avoids the extreme sampling variability of cross validation by using ordinary scale estimators such as the standard deviation and interquartile range which

on parametrizations for the minimal partial realization problem abstract the paper considers parametrizations for minimal partial realizations of a finite sequence of markov parameters first it is shown that all minimal partial realizations have the same set of input and output kronecker indices a parametrization is obtained formulated in terms of a minimal set of extension entries then an i o canonical form is used as a parametrization for the set of minimal partial realizations it is shown that this allows to define the invariants of the problem moreover the i o canonical form is shown to contain a set of parameters that can be chosen such that any minimal partial realization can be represented

linear feedback shift registers and the minimal realization problem the first author is partially supported by grants mineco mtm2017 83624 p mtm2017 90682 redt and upv ehu giu16 42 the second author is partially supported by grants mineco mtm2017 83624 p and mtm2017 90682 redt

on the discrete time integral sliding mode control a new discrete time integral sliding mode control dismc scheme is proposed for sampled data systems the new control scheme is characterized by a discrete time integral sliding manifold which inherits the desired properties of the continuous time integral sliding manifold such as full order sliding manifold with pole assignment and elimination of the reaching phase in particular comparing with existing discrete time sliding mode control the new scheme is able to achieve more precise tracking performance it will be shown in this work that the new control scheme achieves o t2 steady state error for state regulation with the widely adopted delay based disturbance estimation another desirable feature is the proposed dismc prevents the generation of overlarge control actions due to deadbeat response which is usually inevitable due to the existence of poles at the origin for a reduced order sliding manifold designed for sampled data systems both the theoretical analysis and illustrative example demonstrate the validity of the proposed scheme

nonparametric density estimation the l 1 view differentiation of integrals consistency lower bounds for rates of convergence rates of convergence in l1 the automatic kernel estimate l1 and pointwise convergence estimates related to the kernel estimate and the histogram estimate simulation inequalities and random variate generation the transformed kernel estimate applications in discrimination operations on density estimates estimators based on orthogonal series index

quantile hydrologic model selection and model structure deficiency assessment 1 theory a theory for quantile based hydrologic model selection and model structure deficiency assessment is presented the paper demonstrates that the degree to which a model selection problem is constrained by the model structure measured by the lagrange multipliers of the constraints quantifies structural deficiency this leads to a formal definition of model structure deficiency or rigidity model structure deficiency introduces a bias in the prediction of an observed quantile which is often not equal across quantiles structure deficiency is therefore diagnosed when any two quantile predictions for a given model structure cross since unequal bias across quantiles result in quantile predictions crossing the analysis further suggests that the optimal value of quantile specific loss functions order different model structures by its structure deficiencies over a range of quantiles in addition to such novelties quantile hydrologic model selection is a frequentist approach that seeks to complement existing bayesian approaches to hydrological model uncertainty

computationally efficient nonparametric importance sampling the variance reduction established by importance sampling strongly depends on the choice of the importance sampling distribution a good choice is often hard to achieve especially for high dimensional integration problems nonparametric estimation of the optimal importance sampling distribution known as nonparametric importance sampling is a reasonable alternative to parametric approaches in this article nonparametric variants of both the self normalized and the unnormalized importance sampling estimator are proposed and investigated a common critique of nonparametric importance sampling is the increased computational burden compared with parametric methods we solve this problem to a large degree by utilizing the linear blend frequency polygon estimator instead of a kernel estimator mean square error convergence properties are investigated leading to recommendations for the efficient application of nonparametric importance sampling particularly we show that nonparametric importance sampling as

on exponential stabilization for a class of neutral type systems with parameter uncertainties an integral sliding mode approach this paper is focused on integral sliding mode control ismc for a class of parameter uncertain neutral type systems with time varying delays ntstd an integral sliding surface functional is firstly constructed for the ntstd then a delay dependent robust stability criterion is established for all admissible uncertainties which can guarantee the sliding mode dynamic system to be robustly exponentially stable under a certain decay rate in addition by solving the generalized eigenvalue problem gevp presented in the subsequent work the global maximal value of the decay rate as well as the condition of exponential stability for the sliding mode system are derived furthermore a sliding mode control smc law is then synthesised to ensure the stability of the closed loop system and the proposed smc law can drive the state of controlled dynamic system into the predefined sliding surface in finite time finally a numerical example is provided to demonstrate the effectiveness of the obtained results

nonlinear integral type sliding surface for both matched and unmatched uncertain systems this note presents a new nonlinear integral type sliding surface which incorporates a virtual nonlinear nominal control to achieve prescribed specifications first the plant with matched uncertainties is considered the resultant closed loop system during ideal sliding mode behaves exactly like the nominal plant under the nonlinear nominal control which completely nullifies the matched uncertainties and consequently satisfies the prescribed specifications second the stability analysis of the proposed sliding mode for the systems with unmatched uncertainties is performed to exploit the stability conditions numerical results demonstrate the validity of the proposed concept

review papers recent developments in nonparametric density estimation abstract advances in computation and the fast and cheap computational facilities now available to statisticians have had a significant impact upon statistical research and especially the development of nonparametric data analysis procedures in particular theoretical and applied research on nonparametric density estimation has had a noticeable influence on related topics such as nonparametric regression nonparametric discrimination and nonparametric pattern recognition this article reviews recent developments in nonparametric density estimation and includes topics that have been omitted from review articles and books on the subject the early density estimation methods such as the histogram kernel estimators and orthogonal series estimators are still very popular and recent research on them is described different types of restricted maximum likelihood density estimators including order restricted estimators maximum penalized likelihood estimators and sieve estimators are discussed where re

interlacing inequalities and control theory abstract given a p by p matrix a we solve the problem of the existence of a p by q matrix b such that a b has prescribed controllability indices and i p a b has prescribed invariant polynomials the solution of this problem together with an earlier theorem of the author s is used to provide a new proof of the sa thompson interlacing inequalities for invariant polynomials

zero dynamics design and its application to the stabilization of implicit systems abstract we present a formula that computes the output of an r controllable regular single input linear time invariant implicit system in such a way that it has prescribed relative degree and zeros the formula is inspired on different generalizations of ackermann s formula a possible application is in the context of sliding mode control of implicit systems where as the first step one can use the proposed formula to design a sliding surface with desired dynamic characteristics and as the second step apply a higher order sliding mode controller to enforce a sliding motion along the resulting sliding surface

brief variable structure output feedback control design for a class of uncertain dynamic systems in this paper we consider the problem of designing a variable structure output feedback control vsofc law for a class of uncertain systems with mismatched uncertainty in the state matrix we derive existence conditions of linear sliding surfaces in terms of constrained linear matrix inequalities lmis and we give vsofc strategies we also give a simple design procedure and parameterizations of the linear switching surfaces together with solvability conditions for the given constrained lmis finally we give a design example in order to show the effectiveness of our method

sequential monte carlo samplers summary we propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space each distribution being known up to a normalizing constant these probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential monte carlo methods this methodology allows us to derive simple algorithms to make parallel markov chain monte carlo algorithms interact to perform global optimization and sequential bayesian estimation and to compute ratios of normalizing constants we illustrate these algorithms for various integration tasks arising in the context of bayesian inference

local adaptive importance sampling for multivariate densities with strong nonlinear relationships abstract we consider adaptive importance sampling techniques that use kernel density estimates at each iteration as importance sampling functions these can provide more nearly constant importance weights and more precise estimates of quantities of interest than the sampling importance resampling algorithm when the initial importance sampling function is diffuse relative to the target we propose a new method that adapts to the varying local structure of the target when the target has unusual structure such as strong nonlinear relationships between variables this method provides estimates with smaller mean squared error than alternative methods

an lmi based switching surface design method for a class of mismatched uncertain systems in this note we consider the problem of designing linear sliding surfaces for multivariable uncertain systems with mismatched uncertainties in the state matrix in terms of linear matrix inequalities we give a new invariance condition guaranteeing the existence of a linear switching surface such that the reduced order equivalent sliding mode dynamics restricted to the switching surface is not only stable but completely invariant to mismatched uncertainties we also give an explicit formula of such linear switching surfaces together with a design example

discretized and interpolated kernel density estimates abstract the kernel estimate of a probability density function inherits its smoothness properties from the kernel density chosen by the investigator nevertheless for computational and especially graphical reasons exact kernel density estimates are often presented in piecewise constant discretized or piecewise linear interpolated form using the exact estimate only at some grid of points the asymptotic integrated mean squared error properties of these modifications are studied it is seen that discretization may adversely affect the order of magnitude of this risk criterion so care needs to be taken in practice to limit the degree of discretization employed this roughness effect essentially disappears when interpolation is used instead then it takes a remarkably coarse grid to result in more than a negligible deterioration in the estimate s performance data discretization prior to applying the kernel density estimation prescription is also addressed such prebinning does not affect the smoot

on the geometry of realizable markov parameters by simo and miso systems abstract let p m n d be positive integers and let l n d denote the set of sequences l l 1 l n of p m real or complex matrices which are realizable by systems of minimal order d it was shown in 5 14 that l n d can be endowed with a structure of differentiable manifold when p m 1 that is when the sequences are realizable by single input single output siso systems in this paper a similar result is obtained for more general sequences specifically we will consider the set l n r s of sequences l which are realizable by systems of minimal order d and having r and s as brunovsky indices of controllability and observability respectively it is shown in this paper that when one of the two collections of indices r or s is constant then l n r s can be provided with a structure of differentiable manifold and a formula of its dimension is given the special cases r 1 1 or s 1 1 correspond to sequences realizable respectively by single input multi output simo or multi input single output miso systems

monte carlo approximations in bayesian decision theory abstract in decision making problems the bayesian action and its posterior expected loss usually cannot be obtained analytically in this article we study a monte carlo method for approximating the bayesian action and its posterior expected loss the monte carlo approximation to the bayesian action is obtained through a approximating the posterior expected loss function by using the monte carlo integration method and b searching the minimum of the approximated posterior expected loss function as the monte carlo sample size diverges to infinity the monte carlo approximations are shown to be convergent in general situations the rates of the convergence are also obtained under some regularity conditions on the loss function two accuracy measures of the monte carlo approximations are proposed some examples are presented for illustration

bayesian inference in econometric models using monte carlo integration methods for the systematic application of monte carlo integration with importance sampling to bayesian inference in econometric models are developed conditions under which the numerical approximation of a posterior moment converges almost surely to the true value as the number of monte carlo replications increases and the numerical accuracy of this approximation may be assessed reliably are set forth methods for the analytical verification of these conditions are discussed

h sub 2 guaranteed cost switching surface design for sliding modes with nonmatching disturbances this paper presents an extension of utkin and yang s method 1978 for sliding surface design via the minimization of a quadratic performance criterion the method proposed takes into account nonmatching disturbance signals and nonmatching system model uncertainties an upper bound for the h sub 2 norm of the disturbance to output transfer function is minimized leading to a guaranteed cost h sub 2 controller

h controller design for affine fuzzy systems based on piecewise lyapunov functions in finite frequency domain this paper studies the problem of state feedback controller design for a class of nonlinear systems described by continuous time affine fuzzy models based on a piecewise continuous lyapunov function combined with s procedure and some matrix decoupling techniques a novel control law is derived in the formulation of linear matrix inequalities lmis in finite frequency domain first a so called finite frequency h performance index is defined which extends the standard h performance then a sufficient condition is derived such that the fuzzy closed loop system satisfies a finite frequency h performance by introducing the s procedure and adding slack variables piecewise controllers are designed to deal with disturbances in the low middle and high frequency domain respectively the proposed piecewise controller design method can get a better disturbance attenuation performance when the frequency ranges of disturbances are known beforehand finally two examples are given to illustrate the effectiveness and superiority of the new results

lmi based sliding surface design for integral sliding mode control of mismatched uncertain systems we propose a linear matrix inequality lmi based sliding surface design method for integral sliding mode control of mismatched uncertain systems the uncertain system under consideration may have mismatched norm bounded uncertainties in the state matrix as well as the input matrix we give a sufficient condition for the existence of a sliding surface guaranteeing asymptotic stability of the full order sliding mode dynamics we also give an lmi characterization of the sliding surface together with an integral sliding mode control law guaranteeing the existence of a sliding mode from the initial time additionally we give an lmi condition of sliding surfaces guaranteeing the alpha stability constraint finally we give a simulation result to show the effectiveness of our method

minimal state space realization in linear system theory an overview we give a survey of the results in connection with the minimal state space realization problem for linear time invariant systems we start with a brief historical overview and a short introduction to linear system theory next we present some of the basic algorithms for the reduction of nonminimal state space realizations and for the minimal state space realization of infinite or finite sequences of markov parameters of linear time invariant systems finally we discuss some extensions of this problem to other classes of systems and point out some related problems

h sliding mode control for uncertain neutral type stochastic systems with markovian jumping parameters this paper is devoted to the investigation of h sliding mode control smc for uncertain neutral stochastic systems with markovian jumping parameters and time varying delays a sliding surface functional is firstly constructed then the sliding mode control law is designed to guarantee the reachability of the sliding surface in a finite time interval the sufficient conditions for asymptotically stochastic stability of sliding mode dynamics with a given disturbance attenuation level are presented in terms of linear matrix inequalities lmis finally an example is provided to illustrate the efficiency of the proposed method

monte carlo state space likelihoods by weighted posterior kernel density estimation maximum likelihood estimation and likelihood ratio tests for nonlinear non gaussian state space models require numerical integration for likelihood calculations several methods including monte carlo mc expectation maximization mc likelihood ratios direct mc integration and particle filter likelihoods are inefficient for the motivating problem of stage structured population dynamics models in experimental settings an mc kernel likelihood mckl method is presented that estimates classical likelihoods up to a constant by weighted kernel density estimates of bayesian posteriors mckl is derived by using bayesian posteriors as importance sampling densities for unnormalized kernel smoothing integrals mc error and mode bias due to kernel smoothing are discussed and two methods for reducing mode bias are proposed zooming in on the maximum likelihood parameters using a focused prior based on an initial estimate and using a posterior cumulant based approximation of mode bias a simulated example show

differentiable structure of realizable markov parameters abstract the geometry of the finite sequences of p m matrices that are realizable by systems of minimal order d is investigated they can be stratified according to their partial row and column kronecker indices the case when the sum of the number of non zero row and column indices is smaller than the number of elements in the sequence of parameters is considered it is shown that in this case the whole set of markov parameters and each stratum can be endowed with structures of differentiable manifolds and their dimensions are computed

two stage importance sampling with mixture proposals for importance sampling is multiple proposals can be combined to address different aspects of a target distribution there are various methods for is with multiple proposals including hesterberg s stratified is estimator owen and zhou s regression estimator and tan s maximum likelihood estimator for the problem of efficiently allocating samples to different proposals it is natural to use a pilot sample to select the mixture proportions before the actual sampling and estimation however most current discussions are in an empirical sense for such a two stage procedure in this article we establish a theoretical framework of applying the two stage procedure for various methods including the asymptotic properties and the choice of the pilot sample size by our simulation studies these two stage estimators can outperform estimators with naive choices of mixture proportions furthermore while owen and zhou s and tan s estimators are designed for estimating normalizing constants we extend their usa

brief designing robust sliding hyperplanes for parametric uncertain systems a riccati approach in this paper we propose a method to design robust sliding hyperplanes in the presence of mismatched parametric uncertainty based on quadratic stability the robust sliding hyperplane is constructed from a riccati inequality associated with quadratic stabilizability the proposed method enables us to deal with structured uncertainty and optimize the sliding motion by applying the guaranteed cost control idea

robust sliding mode control for uncertain time delay systems an lmi approach this note is devoted to robust sliding mode control for time delay systems with mismatched parametric uncertainties a delay independent sufficient condition for the existence of linear sliding surfaces is given in terms of linear matrix inequalities based on which the corresponding reaching motion controller is also developed the results are illustrated by an example

on minimality in the partial realization problem abstract a compression algorithm is constructed which allows one to reduce an arbitrary realization of a finite sequence of matrices m 1 m r to a minimal partial realization of m 1 m r furthermore a state space criterion of minimality of a partial realization and a formula for the minimal state space dimension are obtained

technical communique on the existence of linear sliding surfaces for a class of uncertain dynamic systems with mismatched uncertainties recently choi 1998 automatica 34 8 1015 1020 has considered the problem of designing a variable structure control vsc law for uncertain systems where the uncertainty in the state model does not satisfy the matching condition he has given a sufficient condition for the existence of linear sliding surfaces in terms of lmis in this note we show that the lmi existence condition given in the above paper is a necessary and sufficient one and we give lmi existence conditions of a sliding surface guaranteeing general lmi regional pole placement constraints while in the above paper pole placement only in a vertical half plane and a disk has been considered finally we give simulation results for controlling an inverted pendulum mounted on a cart

the calculation of posterior distributions by data augmentation abstract the idea of data augmentation arises naturally in missing value problems as exemplified by the standard ways of filling in missing cells in balanced two way tables thus data augmentation refers to a scheme of augmenting the observed data so as to make it more easy to analyze this device is used to great advantage by the em algorithm dempster laird and rubin 1977 in solving maximum likelihood problems in situations when the likelihood cannot be approximated closely by the normal likelihood maximum likelihood estimates and the associated standard errors cannot be relied upon to make valid inferential statements from the bayesian point of view one must now calculate the posterior distribution of parameters of interest if data augmentation can be used in the calculation of the maximum likelihood estimate then in the same cases one ought to be able to use it in the computation of the posterior distribution it is the purpose of this article to explain how this can be done the basic idea