algebraic tensegrity form finding abstract this paper concerns the form finding problem for general and symmetric tensegrity structures with shape constraints a number of different geometries are treated and several fundamental properties of tensegrity structures are identified that simplify the form finding problem the concept of a tensegrity invariance similarity transformation is defined and it is shown that tensegrity equilibrium is preserved under affine node position transformations this result provides the basis for a new tensegrity form finding tool the generality of the problem formulation makes it suitable for the automated generation of the equations and their derivatives state of the art numerical algorithms are applied to solve several example problems examples are given for tensegrity plates shell class symmetric tensegrity structures and structures generated by applying similarity transformation

almost invariant subspaces an approach to high gain feedback design part ii almost conditionally invariant subspaces in part i of this paper almost controlled invariant subspaces were studied in this part we will consider their duals the almost conditionally invariant subspaces these concepts give immediately by dualization of the almost disturbance decoupling control by state feedback the solution of the almost disturbance decoupled estimation problem finally we consider the problem of approximate disturbance decoupling by measurement feedback and it is shown that this problem is solvable to any arbitrary degree of accuracy if and only if 1 almost disturbance decoupling by state feedback and 2 almost disturbance decoupled estimation of the to be controlled output are both possible

a state space description for perfect reconstruction wavelet fir filter banks with special orthonormal basis functions this paper presents a state space description for wavelet fir filter banks with perfect reconstruction using special orthonormal basis functions the fir structure guarantees the bibo stability robustness and improves the filter divergence problem while orthonormal basis functions have characteristics that make them attractive in the modeling of dynamic systems the state space description presented in this paper has all of those advantages and is minimal

a monte carlo form finding method for large scale regular and irregular tensegrity structures we propose a monte carlo form finding method that employs a stochastic procedure to determine equilibrium configurations of a tensegrity structure this method does not involve complicated matrix operations or symmetry analysis works for arbitrary initial configurations and can handle large scale regular or irregular tensegrity structures with or without material geometrical constraints

bayesian semiparametric wiener system identification we present a novel method for wiener system identication the method relies on a semiparametric i e a mixed parametric nonparametric model of a wiener system we use a state space model for the linear dynamical system and a nonparametric gaussian process model for the static nonlinearity we avoid making strong assumptions such as monotonicity on the nonlinear mapping stochastic disturbances entering both as measurement noise and as process noise are handled in a systematic manner the nonparametric nature of the gaussian process allows us to handle a wide range of nonlinearities without making problem specic parameterizations we also consider sparsity promoting priors based on generalized hyperbolic distributions to automatically infer the order of the underlying dynamical system we derive an inference algorithm based on an ecient particle markov chain monte carlo method referred to as particle gibbs with ancestor sampling the method is proled on two challenging identication problems with good results blind wiener system identication is handled as a special case

system structure and singular control the general continuous time linear quadratic control problem is considered it is shown that recently developed linear system theoretic properties and algorithms play an important role in solving this singular control problem

paper zeros of sampled systems the zeros of the discrete time system obtained when sampling a continuous time system are explored theorems for the limiting zeros for large and small sampling periods are given conditions which guarantee that the sampled system has stable zeros are also presented

tensegrity deployment using infinitesimal mechanisms abstract kinematic properties of tensegrity structures reveal that an ideal way of motion is by using their infinitesimal mechanisms for example in motions along infinitesimal mechanisms there is no energy loss due to linearly kinetic tendon damping consequently a deployment strategy which exploits these mechanisms and uses the structure s nonlinear equations of motion is developed desired paths that are tangent to the directions determined by infinitesimal mechanisms are constructed and robust nonlinear feedback control is used for accurate tracking of these paths examples demonstrate the feasibility of this approach and further analysis reveals connections between the power and energy dissipated via damping infinitesimal mechanisms speed of the motion and deployment time

bounded error identification of hammerstein systems through sparse polynomial optimization in this paper we present a procedure for the evaluation of bounds on the parameters of hammerstein systems from output measurements affected by bounded errors the identification problem is formulated in terms of polynomial optimization and relaxation techniques based on linear matrix inequalities are proposed to evaluate parameter bounds by means of convex optimization the structured sparsity of the formulated identification problem is exploited to reduce the computational complexity of the convex relaxed problem analysis of convergence properties and computational complexity is reported

continuous time hammerstein system identification a continuous time hammerstein system i e a system consisting of a nonlinear memoryless subsystem followed by a linear dynamic one is identified the system is driven and disturbed by white random signals the a priori information about both subsystems is nonparametric which means that functional forms of both the nonlinear characteristic and the impulse response of the dynamic subsystem are unknown an algorithm to estimate the nonlinearity is presented and its pointwise convergence to the true characteristic is shown the impulse response of the dynamic part is recovered with a correlation method the algorithms are computationally independent results of a simulation example are given

convergence analysis of recursive identification algorithms based on the nonlinear wiener model recursive identification algorithms based on the nonlinear wiener model are presented a recursive identification algorithm is first derived from a general parameterization of the wiener model using a stochastic approximation framework local and global convergence of this algorithm can be tied to the stability properties of an associated differential equation since inversion is not utilized noninvertible static nonlinearities can be handled which allows a treatment of for example saturating sensors and blind adaptation problems gauss newton and stochastic gradient algorithms for the situation where the static nonlinearity is known are then suggested in the single input single output case the proposed methods can outperform conventional linearizing inversion of the nonlinearity when measurement disturbances affect the output signal for fir finite impulse response models it is also proved that global convergence of the schemes is tied to sector conditions on the static nonlinearity in particular global convergence of the stochastic gradient method is obtained provided that the nonlinearity is strictly monotone the local analysis performed for iir infinite impulse response models illustrates the importance of the amplitude contents of the exciting signals

when is a symmetric pin jointed framework isostatic maxwell s rule from 1864 gives a necessary condition for a framework to be isostatic in 2d or in 3d given a framework with point group symmetry group representation theory is exploited to provide further necessary conditions this paper shows how for an isostatic framework these conditions imply very simply stated restrictions on the numbers of those structural components that are unshifted by the symmetry operations of the framework in particular it turns out that an isostatic framework in 2d can belong to one of only six point groups some conjectures and initial results are presented that would give sufficient conditions in both 2d and 3d for a framework that is realized generically for a given symmetry group to be an isostatic framework

analytic study on the intrinsic zeros of sampled data systems this paper investigates the properties of the mapping from the simple zero spl gamma of a scalar continuous time system to the corresponding zero spl gamma t of the sampled data system that results by its discretization using a zero order hold where t is the sampling period it is shown that spl gamma t admits a taylor expansion with respect to t and that it coincides with that of exp spl gamma t at least up to the second order term in general and at least up to the third order term if the relative degree of the continuous time system is greater than or equal to two the result is applied to derive a new stability condition of spl gamma t for sufficiently small sampling periods

risk adjusted set membership identification of wiener systems this technical note addresses the problem of set membership identification of wiener systems its main result shows that even though the problem is generically np hard it can be reduced to a tractable convex optimization through the use of risk adjusted methods in addition this approach allows for efficiently computing worst case bounds on the identification error finally we provide an analysis of the intrinsic limitations of interpolatory algorithms these results are illustrated with a non trivial problem arising in computer vision tracking a human in a sequence of frames where the challenge here arises from the changes in appearance undergone by the target and the large number of pixels to be tracked

decoupling the linear and nonlinear parts in hammerstein model identification the main result of this paper is to show that the linear part can be made decoupled from the nonlinear part in hammerstein model identification therefore identification of the linear part for a hammerstein model becomes a linear problem and accordingly enjoys the same convergence and consistency results as if the unknown nonlinearity is absent

an optimal two stage identification algorithm for hammerstein wiener nonlinear systems abstract an optimal two stage identification algorithm is presented for hammerstein wiener systems where two static nonlinear elements surround a linear block the proposed algorithm consists of two steps the first one is the recursive least squares and the second one is the singular value decomposition of two matrices whose dimensions are fixed and do not increase as the number of the data point increases moreover the algorithm is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise

computational load reduction in bounded error identification of hammerstein systems in this technical note we present a procedure for the identification of hammerstein systems from measurements affected by bounded noise first we show that computation of tight parameter bounds requires the solution to nonconvex optimization problems where the number of decision variables increases with the length of the experimental data sequence then in order to reduce the computational burden of the identification problem we propose a procedure to relax the formulated problem into a collection of polynomial optimization problems where the number of variables does not depend on the number of measurements advantages of the presented approach with respect to previously published results are discussed and highlighted by means of a simulation example

a new approach to the analytical and numerical form finding of tensegrity structures we develop a new formulation for the form finding of tensegrity structures in which the primary variables are the cartesian components of element lengths both an analytical and a numerical implementation of the formulation are described each require a description of the connectivity of the tensegrity with the iterative numerical method also requiring a random starting vector of member force densities the analytical and numerical form finding of tensegrity structures is demonstrated through six examples and the results obtained are compared and contrasted with those available in the literature to verify the accuracy and viability of the suggested methods

particle markov chain monte carlo methods summary markov chain monte carlo and sequential monte carlo methods have emerged as the two main tools to sample from high dimensional probability distributions although asymptotic convergence of markov chain monte carlo algorithms is ensured under weak assumptions the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and or if highly correlated variables are updated independently we show here how it is possible to build efficient high dimensional proposal distributions by using sequential monte carlo methods this allows us not only to improve over standard markov chain monte carlo schemes but also to make bayesian inference feasible for a large class of statistical models where this was not previously so we demonstrate these algorithms on a non linear state space model and a levy driven stochastic volatility model

solution of the static state feedback decoupling problem for linear systems with two outputs we give for linear systems with two outputs a necessary and sufficient condition for the solvability of the restricted decoupling problem rdp in terms of some structural invariants a geometric characterization is also given which provides the least dynamical extension needed when rdp is not solvable

sampling infinite zeros and decoupling of linear systems abstract in order to understand more fully some of the trade offs involved in using a sampled data representation of a continuous time system the effects of time sampling on the ability to achieve disturbance decoupling and input output decoupling for linear systems are investigated it is shown that disturbance decouplability is lost through sampling whereas row by row dynamic input output decouplability is preserved in a very strong way these results are obtained by analyzing the structure at infinity of a sampled data system

symmetric prismatic tensegrity structures part i configuration and stability this paper presents a simple and efficient method to determine the self equilibrated configurations of prismatic tensegrity structures nodes and members of which have dihedral symmetry it is demonstrated that stability of this class of structures is not only directly related to the connectivity of members but is also sensitive to their geometry height radius ratio and is also dependent on the level of self stress and stiffness of members a catalogue of the structures with relatively small number of members is presented based on the stability investigations

a markov chain monte carlo approach to nonlinear parametric system identification nonlinear system identification is discussed in a mixed set membership and statistical setting a markov chain monte carlo mcmc approach is proposed that estimates the feasible parameter set the minimum volume outer bounding ellipsoid and the minimum variance estimate the proposed algorithm is proved to be convergent and enjoys some desirable properties further its computational complexity and numerical accuracy are studied

topology design of tensegrity structures via mixed integer programming this paper presents a numerical method for finding a tensegrity structure based on the ground structure method we first solve a mixed integer programming mip problem which maximizes the number of struts over the self equilibrium condition of axial forces and the discontinuity condition of struts subsequently we solve the minimization problem of the number of cables in order to remove redundant self equilibrium modes which is also formulated as an mip it is regarded to be advantageous that our method does not require any connectivity information of cables and struts to be known in advance while the obtained tensegrity structure is guaranteed to satisfy the discontinuity condition of struts rigorously

blind maximum likelihood identification of wiener systems this paper is about the identification of discrete time wiener systems from output measurements only blind identification assuming that the unobserved input is white gaussian noise that the static nonlinearity is invertible and that the output is observed without errors a gaussian maximum likelihood estimator is constructed its asymptotic properties are analyzed and the cramer rao lower bound is calculated a two step procedure for generating high quality initial estimates is presented as well the paper includes the illustration of the method on a simulation example

the bayesian lasso the lasso estimate for linear regression parameters can be interpreted as a bayesian posterior mode estimate when the regression parameters have independent laplace i e double exponential priors gibbs sampling from this posterior is possible using an expanded hierarchy with conjugate normal priors for the regression parameters and independent exponential priors on their variances a connection with the inverse gaussian distribution provides tractable full conditional distributions the bayesian lasso provides interval estimates bayesian credible intervals that can guide variable selection moreover the structure of the hierarchical model provides both bayesian and likelihood methods for selecting the lasso parameter slight modifications lead to bayesian versions of other lasso related estimation methods including bridge regression and a robust variant

a direct approach to design of geometry and forces of tensegrity systems in the process of designing a tensegrity system some constraints are usually introduced for geometry and or forces to ensure uniqueness of the solution because the tensegrity systems are underdetermined in most cases in this paper a new approach is presented to enable designers to specify independent sets of axial forces and nodal coordinates consecutively under the equilibrium conditions and the given constraints to satisfy the distinctly different requirements of architects and structural engineers the proposed method can be used very efficiently for practical applications because only linear algebraic equations are to be solved and no equation of kinematics or material property is needed some numerical examples are given to show not only efficiency of the proposed method but also its ability of searching new configurations

a semiparametric bernstein von mises theorem for gaussian process priors this paper is a contribution to the bayesian theory of semiparametric estimation we are interested in the so called bernstein von mises theorem in a semiparametric framework where the unknown quantity is f with the parameter of interest and f an infinite dimensional nuisance parameter two theorems are established one in the case with no loss of information and one in the information loss case with gaussian process priors the general theory is applied to three specific models the estimation of the center of symmetry of a symmetric function in gaussian white noise a time discrete functional data analysis model and cox s proportional hazards model in all cases the range of application of the theorems is investigated by using a family of gaussian priors parametrized by a continuous parameter

approximation of the feasible parameter set in worst case identification of hammerstein models the estimation of the feasible parameter set fps for hammerstein models in a worst case setting is considered a bounding procedure is determined both for polytopic and ellipsoidic uncertainties it consists in the projection of the fps of the extended parameter vector onto suitable subspaces and in the solution of convex optimization problems which provide uncertainties intervals of the model parameters the bounds obtained are tighter than in the previous approaches hes

a two stage algorithm for identification of nonlinear dynamic systems this paper investigates the two stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear in the parameters models and the model has to be built from a very large pool of basis functions or model terms the main objective is to improve the compactness of the model that is obtained by the forward stepwise methods while retaining the computational efficiency the proposed algorithm first generates an initial model using a forward stepwise procedure the significance of each selected term is then reviewed at the second stage and all insignificant ones are replaced resulting in an optimised compact model with significantly improved performance the main contribution of this paper is that these two stages are performed within a well defined regression context leading to significantly reduced computational complexity the efficiency of the algorithm is confirmed by the computational complexity analysis and its effectiveness is demonstrated by the simulation results

adaptive force density method for form finding problem of tensegrity structures a numerical method is presented for form finding of tensegrity structures eigenvalue analysis and spectral decomposition are carried out iteratively to find the feasible set of force densities that satisfies the requirement on rank deficiency of the equilibrium matrix with respect to the nodal coordinates the equilibrium matrix is shown to correspond to the geometrical stiffness matrix in the conventional finite element formulation a unique and non degenerate configuration of the structure can then be obtained by specifying an independent set of nodal coordinates a simple explanation is given for the required rank deficiency of the equilibrium matrix that leads to a non degenerate structure several numerical examples are presented to illustrate the robustness as well as the strong ability of searching new configurations of the proposed method

consistent identification of wiener systems a machine learning viewpoint wiener system identification has been recently performed by adopting a bayesian semiparametric approach in this framework the linear system entering the first block is given a finite dimensional parametrization while nonparametric gaussian regression is used to estimate the static nonlinearity in the second block in this paper we study the asymptotic behavior of this estimator when the number of noisy output samples tends to infinity without assuming the correctness of the bayesian prior models for this purpose we interpret wiener identification under a machine learning perspective this allows us to extend recent results on function estimation in reproducing kernel hilbert spaces to derive a condition guaranteeing the statistical consistency of the identification procedure we also discuss how the violation of such a condition can lead to useless estimates of the wiener structure

subspace based approaches for wiener system identification we consider the problem of wiener system identification in this note a wiener system consists of a linear time invariant block followed by a memoryless nonlinearity by modeling the inverse of the memoryless nonlinearity as a linear combination of known nonlinear basis functions we develop two subspace based approaches namely an alternating projection algorithm and a minimum norm method to solve for the wiener system parameters based on computer simulations the algorithms are shown to be robust in the presence of modeling error and noise

mappings of the finite and the infinite zero structures and invertibility structures of general linear multivariable systems under the bilinear transformation this paper presents a comprehensive picture of the mapping of structural properties associated with general linear multivariable systems under bilinear transformation while the mapping of poles of linear multivariable systems under such a transformation is well known the question of how the structural invariant properties of a given system are mapped remains unanswered this paper addresses this question more specifically we investigate how the finite and infinite zero structures as well as invertibility structures of a general continuous time discrete time linear time invariant multivariable system are mapped to those of its discrete time continuous time counterpart under the bilinear inverse bilinear transformation we demonstrate that the structural invariant indices lists i2 and i3 of morse remain invariant under the bilinear transformation while the structural invariant indices lists i1 and i4 of morse are in general changed

frequency domain identification of wiener models this paper proposes a frequency domain algorithm for wiener model identifications based on exploring the fundamental frequency and harmonics generated by the unknown nonlinearity the convergence of the algorithm is established in the presence of white noise no a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric

bounding the parameters of linear systems with input backlash in this note we present a two stage procedure for deriving parameters bounds of linear systems with input backlash when the output measurement errors are bounded first using steady state input output data parameters of the nonlinear dynamic block are tightly bounded then given a suitable prbs input sequence we evaluate tight bounds on the unmeasurable inner signal which together with noisy output measurements are employed for bounding the parameters of the linear dynamic system

brief paper computational complexity analysis of set membership identification of hammerstein and wiener systems this paper analyzes the computational complexity of set membership identification of hammerstein and wiener systems its main results show that even in cases where a portion of the plant is known the problems are generically np hard both in the number of experimental data points and in the number of inputs wiener or outputs hammerstein of the nonlinearity these results provide new insight into the reasons underlying the high computational complexity of several recently proposed algorithms and point out the need for developing computationally tractable relaxations

adaptive nonlinear neuro controller with an integrated evaluation algorithm for nonlinear active noise systems an adaptive nonlinear neuro controller with an integrated evaluation algorithm for nonlinear active noise control systems is proposed to attenuate the nonlinear and non gaussian noises inspired by the structure of the hammerstein or wiener model the proposed controller is realized by the static nonlinear memory function mapping on the basis of a single neuron a generalized filtered x gradient descent algorithm based on an integrated evaluation criterion is developed to adaptively adjust the weights of the controller where the weighted sum of renyi s quadratic error entropy and the mean square error is applied as the integrated performance index which improves the performance of the adaptive algorithm by introducing the information entropy in addition the convergence of the proposed approach is analyzed and the computational complexity among different methods is investigated the proposed scheme can effectively attenuate the nonlinear and non gaussian noises and has a relative simple structure and less learning parameters the simulation results demonstrate the validity of the proposed method for attenuating the nonlinear and non gaussian noises

parameter bounds for discrete time hammerstein models with bounded output errors in this note we present a two stage procedure for deriving parameters bounds in hammerstein models when the output measurement errors are bounded first using steady state input output data parameters of the nonlinear part are tightly bounded then for a given input transient sequence we evaluate tight bounds on the unmeasurable inner signal which together with noisy output measurements are used for bounding the parameters of the linear dynamic block

limiting zeros of decouplable mimo systems for sufficiently rapid zero order hold zoh sampling it is known that the zeros of single input single output discrete time systems expressed in the forward shift operator converge to values determined only by the degrees of the finite and infinite zeros of the underlying continuous time system in this paper it is shown how this result can be generalized to multi input multi output mimo systems decouplable by static state feedback equivalently having a diagonal interactor matrix in the fast sampling limit the authors show how invariant and infinite zero structure is mapped under zoh sampling for discrete time systems expressed in either the delta forward difference or forward shift operators

on the structure at infinity of linear block decouplable systems the general case the aim of this note is to show that a linear system c a b is block decouplable by means of static state feedback laws f g with g invertible if and only if the infinite structure of c a b equals the union of the infinite structures of the subsystems c i a b extracted from the given output partition this result generalizes the ones recently obtained for morgan s problem 6 and for the particular cases of right invertible 7 or left invertibie 8 systems

frequency domain identification of hammerstein models discusses hammerstein model identification in the frequency domain using sampled input output data by exploring the fundamental frequency and harmonics generated by the unknown nonlinearity we propose a frequency domain approach and show its convergence for both the linear and nonlinear subsystems in the presence of noise no a priori knowledge of the structure of the nonlinearity is required and the linear part can be nonparametric

identification of block oriented nonlinear systems starting from linear approximations a survey abstract block oriented nonlinear models are popular in nonlinear system identification because of their advantages of being simple to understand and easy to use many different identification approaches were developed over the years to estimate the parameters of a wide range of block oriented nonlinear models one class of these approaches uses linear approximations to initialize the identification algorithm the best linear approximation framework and the approximation framework or equivalent frameworks allow the user to extract important information about the system guide the user in selecting good candidate model structures and orders and prove to be a good starting point for nonlinear system identification algorithms this paper gives an overview of the different block oriented nonlinear models that can be identified using linear approximations and of the identification algorithms that have been developed in the past a non exhaustive overview of the most important other block oriented nonlinear system identification approaches is also provided throughout this paper

nonparametric identification of wiener systems a wiener system i e a system in which a linear dynamic part is followed by a nonlinear and memoryless one is identified no parametric restriction is imposed on the functional form of the nonlinear characteristic of the memoryless subsystem and a nonparametric algorithm recovering the characteristic from input output observations of the whole system is proposed its consistency is shown and the rate of convergence is given an idea for identification of the impulse response of the linear subsystem is proposed results of numerical simulation are also presented

identification of approximated hammerstein models in a worst case setting the identification of hammerstein models for nonlinear systems in considered in a worst case setting assuming unknown but bounded measurement noise a new approach is proposed in which the identification of a low complexity hammerstein model amounts to the computation of the chebichev center of a set of matrices conditioned to the manifold of rank one matrices an identification algorithm based on a relaxation technique is proposed and its consistency is proven the algorithm is computationally attractive in two cases noise bounded either in 2 or in norm the effectiveness of the proposed central algorithm and the comparison with the corresponding projection algorithm which is based on the singular value decomposition are investigated both analytically and through numerical examples in particular tight error bounds are obtained for the projection algorithm

nonlinear programming approach to form finding and folding analysis of tensegrity structures using fictitious material properties abstract an optimization approach is presented for form finding of tensegrity structures it is shown that various equilibrium shapes can be easily found by solving a forced deformation analysis problem formulated as a minimization problem considering the nodal coordinates as design variables the objective function is defined in terms of the member lengths and it can be regarded as the total strain energy corresponding to fictitious elastic material properties the self equilibrium forces can be found from the optimality conditions of the nonlinear programming problem stability of the self equilibrium shape is investigated based on the local convexity of the objective function similarity between form finding problem of a structure with zero unstressed length cables and the problem of minimum square length network is also discussed furthermore folding of a structure with small unstressed length cables is approximately simulated using affine transformation of equilibrium shape

identification of hammerstein wiener models this paper develops and illustrates a new maximum likelihood based method for the identification of hammerstein wiener model structures a central aspect is that a very general situation is considered wherein multivariable data non invertible hammerstein and wiener nonlinearities and colored stochastic disturbances both before and after the wiener nonlinearity are all catered for the method developed here addresses the blind wiener estimation problem as a special case

bandpass nonlinear systems identification by higher order cross correlation nonlinear systems constituted by a zero memory nonlinearity cascaded with linear filters can be identified by input output cross correlation using a gaussian input signal the method is extended to complex systems through a pair of complex invariance theorems the stated properties allow identifying the linear parts of systems characterized by magnitude phase nonlinearities with the joint use of second and third order input output moments the method can be employed for a wide class of communication bandpass circuits when signals are represented by complex envelopes

interactor structure estimation for adaptive control of discrete time multivariable nondecouplable systems abstract we are interested in this paper in adaptively controlling nondecouplable discrete time multivariable systems with controllers of reduced complexity and without introducing additional delays in the loop we provide a solution to the following problems first give a procedure to incorporate plant prior information to reduce the number of adjustable parameters to this end we propose an algorithm for the estimation of the interactor structure from generic knowledge of the plant markov parameters second present a globally convergent adaptive controller for minimum phase systems with nontrivial delay structure also we give a self contained elementary derivation of the delay structure of multivariable systems in terms of matchable models and the plant markov parameters

research on system zeros a survey this report provides a review of notable publications regarding the topic of zeros of multivariable systems in the period from 1970 into 1989 particular attention is paid to relationships between definitions of such zeros and their theoretical development transmission invariant system module infinite and finite zeros are addressed the latter subdivided into a hierarchical structure with respect to information considered in the definition process

partially collapsed gibbs samplers theory and methods ever increasing computational power along with ever more sophisticated statistical computing techniques is making it possible to fit ever more complex statistical models among the more computationally intensive methods the gibbs sampler is popular because of its simplicity and power to effectively generate samples from a high dimensional probability distribution despite its simple implementation and description however the gibbs sampler is criticized for its sometimes slow convergence especially when it is used to fit highly structured complex models here we present partially collapsed gibbs sampling strategies that improve the convergence by capitalizing on a set of functionally incompatible conditional distributions such incompatibility generally is avoided in the construction of a gibbs sampler because the resulting convergence properties are not well understood we introduce three basic tools marginalization permutation and trimming that allow us to transform a gibbs sampler into a part

identification scheme for hammerstein output error models with bounded noise this technical note presents a method for the identification of hammerstein output error hoe models corrupted by bounded noise the method is based on an iterative scheme using two optimal bounding ellipsoid obe type algorithms the proposed approach allows the joint estimation of the linear part and of the nonlinear part with a low computational burden a numerical example is given so as to show the efficiency of the method