a blind approach to hammerstein model identification this paper discusses the hammerstein model identification using a blind approach by fast sampling at the output it is shown that identification of the linear part can be achieved based only on the output measurements that makes the hammerstein model identification possible without knowing the structure of the nonlinearity and the internal variables

differential algebraic systems as differential equations on manifolds based on the theory of differential equations on manifolds existence and uniqueness results are proved for a class of mixed systems of differential and algebraic equations as they occur in various applications both the autonomous and nonautonomous case are considered moreover a class of algebraically incomplete systems is introduced for which existence and uniqueness results only hold on certain lower dimensional manifolds this class includes systems for which the application of ode solvers is known to lead to difficulties finally some solution approach based on continuation techniques is outlined

multiple rademacher means and their applications abstract we evaluate multiple rademacher means for various classes of multilinear operators under the assumption that the range has cotype q these results show that the class of multiple q 2 summing operators plays an important role further we get new extensions of bu s theorem in the multilinear case

on the cohen strongly p summing multilinear operators abstract in this paper we introduce and study a new concept of summability in the category of multilinear operators which is the cohen strongly p summing multilinear operators we prove a natural analog of the pietsch domination theorem and we compare the notion of p dominated multilinear operators with this class by generalizing a theorem of bu cohen

a geometric treatment of implicit differential algebraic equations abstract a differential geometric approach for proving the existence and uniqueness of implicit differential algebraic equations is presented it provides for a significant improvement of an earlier theory developed by the authors as well as for a completely intrinsic definition of the index of such problems the differential algebraic equation is transformed into an explicit ordinary differential equation by a reduction process that can be abstractly defined for specific submanifolds of tangent bundles here called reducible submanifolds local existence and uniqueness results for differential algebraic equations then follow directly from the final stage of this reduction by means of an application of the standard theory of ordinary differential equations

brief paper convergence of the iterative algorithm for a general hammerstein system identification the convergence of the iterative identification algorithm for a general hammerstein system has been an open problem for a long time in this paper it is shown that the convergence can be achieved by incorporating a regularization procedure on the nonlinearity in addition to a normalization step on the parameters

subspace identification methods for hammerstein systems rank constraint and dimension problem this article studies the subspace identification methods sims for hammerstein systems with major focus on a rank constraint and the related dimension problem we analyse the effects of the rank constraint on the three steps of a unifying framework for sims the rank constraint has no effect on the first two steps but does so on the third step if the rank constraint is ignored as in the existing over parametrised method opm for hammerstein system identification the optimality of the resulting estimate can still be established even so the opm may suffer from the dimension problem resulting in a low numerical efficiency to resolve the dimension problem we propose a new subspace based method named as the least parametrised method lpm for identification of hammerstein systems with non coupling input nonlinearities simulation results are provided to demonstrate the effectiveness of the lpm and show the necessity of considering the rank constraint to improve the numerical efficiency

adaptive multiregression in reproducing kernel hilbert spaces the multiaccess mimo channel case this paper introduces a wide framework for online i e time adaptive supervised multiregression tasks the problem is formulated in a general infinite dimensional reproducing kernel hilbert space rkhs in this context a fairly large number of nonlinear multiregression models fall as special cases including the linear case any convex continuous and not necessarily differentiable function can be used as a loss function in order to quantify the disagreement between the output of the system and the desired response the only requirement is the subgradient of the adopted loss function to be available in an analytic form to this end we demonstrate a way to calculate the subgradients of robust loss functions suitable for the multiregression task as it is by now well documented when dealing with online schemes in rkhs the memory keeps increasing with each iteration step to attack this problem a simple sparsification strategy is utilized which leads to an algorithmic scheme of linear complexity with respect to the number of unknown parameters a convergence analysis of the technique based on arguments of convex analysis is also provided to demonstrate the capacity of the proposed method the multiregressor is applied to the multiaccess multiple input multiple output channel equalization task for a setting with poor resources and nonavailable channel information numerical results verify the potential of the method when its performance is compared with those of the state of the art linear techniques which in contrast use space time coding more antenna elements as well as full channel information

multiple summing operators on banach spaces abstract in this paper we improve some previous results about multiple p summing multilinear operators by showing that every multilinear form from l 1 spaces is multiple p summing for 1 p 2 the proof is based on the existence of a predual for the banach space of multiple p summing multilinear forms we also show the failure of the inclusion theorem in this class of operators and improve some results of y melendez and a tonge about dominated multilinear operators

strongly p summing multilinear operators abstract we define the concept of strongly p summing multilinear operators and prove that they have properly defined aron berner extensions some properties and examples are shown

on the computation of impasse points of quasi linear differential algebraic equations abstract we present computational algorithms for the calculation of impasse points and higher order singularities in quasilinear differential algebraic equations our method combines a reduction step transforming the dae into a singular ode with an augmentation procedure inspired by numerical bifurcation theory singularities are characterized by the vanishing of a scalar quantity that may be monitored along any trajectory two numerical examples with physical relevance are given

identification of mimo hammerstein models using least squares support vector machines this paper studies a method for the identification of hammerstein models based on least squares support vector machines ls svms the technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic arx part this is done by applying the equivalent of bai s overparameterization method for identification of hammerstein systems in an ls svm context the siso as well as the mimo identification cases are elaborated the technique can lead to significant improvements with respect to classical overparameterization methods as illustrated in a number of examples another important advantage is that no stringent assumptions on the nature of the nonlinearity need to be imposed except for a certain degree of smoothness

identification of discrete hammerstein systems using kernel regression estimates in this note a discrete time hammerstein system is identified the weighting function of the dynamical subsystem is recovered by the correlation method the main results concern estimation of the nonlinear memoryless subsystem no conditions concerning functional form of the transform characteristic of the subsystem are made and an algorithm for estimation of the characteristic is presented the algorithm is a nonparametric kernel estimate of regression functions calculated from dependent data it is shown that the algorithm converges to the characteristic as the number of observations tend to infinity for sufficiently smooth characteristics the rate of convergence is o n 2 5 in probability

identification of state dependent parameter models with support vector regression a support vector regression approach is presented for the identification of state dependent parameter arx models whose parameters are described as functions of past inputs and outputs the problem of identifying the state dependent parameters reduces to a standard support vector regression problem with a kernel function which is defined in terms of the kernels used to represent the individual parameters numerical examples show that the support vector method gives accurate parameter estimates for systems which have a state dependent parameter representation

identifying mimo hammerstein systems in the context of subspace model identification methods in this paper we outline the extension of the moesp family of subspace model identification schemes to the hammerstein type of nonlinear system two types of identification problem are considered the first type assumes the polynomial structure of the static nonlinearity to be given and the task is to identify both the linear system dynamics and the unknown proportional constants in the para metrization of the static nonlinearity the second type addresses the identification of both the linear dynamic part and the static nonlinearity where only limited a priori information regarding the structure of the nonlinearity is available the improved robustness properties of the algorithms developed for this second type of hammerstein identification problem over existing correlation based schemes is illustrated by a numerical example

constrained equations with impasse points abstract we give local normal forms for generic constrained equations of the form f z z h z wherefis a smooth real function defined on a manifoldmandhis a smooth vector field onm

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

nonlinear black box models in system identification mathematical foundations we discuss several aspects of the mathematical foundations of the nonlinear black box identification problem we shall see that the quality of the identification procedure is always a result of a certain trade off between the expressive power of the model we try to identify the larger the number of parameters used to describe the model the more flexible is the approximation and the stochastic error which is proportional to the number of parameters a consequence of this trade off is the simple fact that a good approximation technique can be the basis of a good identification algorithm from this point of view we consider different approximation methods and pay special attention to spatially adaptive approximants we introduce wavelet and neuron approximations and show that they are spatially adaptive then we apply the acquired approximation experience to estimation problems finally we consider some implications of these theoretical developments for the practically implemented versions of the spatially adaptive algorithms

nonparametric identification of wiener systems by orthogonal series a wiener system i e a system comprising a linear dynamic and a nonlinear memoryless subsystems connected in a cascade is identified both the input signal and disturbance are random white and gaussian the unknown nonlinear characteristic is strictly monotonous and differentiable and therefore the problem of its recovering from input output observations of the whole system is nonparametric it is shown that the inverse of the characteristic is a regression function and a class of orthogonal series nonparametric estimates recovering the regression is proposed and analyzed the estimates apply the trigonometric legendre and hermite orthogonal functions pointwise consistency of all the algorithms is shown under some additional smoothness restrictions the rates of their convergence are examined and compared an algorithm to identify the impulse response of the linear subsystem is proposed

on implicit systems of differential equations abstract this article considers implicit systems of differential equations the implicit systems that are considered are given by polynomial relations on the coordinates of the indeterminate function and the coordinates of the time derivative of the indeterminate function for such implicit systems of differential equations we are concerned with computing algebraic constraints such that on the algebraic variety determined by the constraint equations the original implicit system of differential equations has an explicit representation our approach is algebraic although there have been a number of articles that approach implicit differential equations algebraically all such approaches have relied heavily on linear algebra the approach of this article is different we have no linearity requirements at all instead we rely on algebraic geometry in particular we use birational mappings to produce an explicit system the methods developed in this article are easily implemented using various computer algebra systems

an abstract result on cohen strongly summing operators abstract we present an abstract result that characterizes the coincidence of certain classes of linear operators with the class of cohen strongly summing linear operators our argument is extended to multilinear operators and as a consequence we establish some alternative characterizations for the class of cohen strongly summing multilinear operators

finite model order accuracy in hammerstein model estimation hammerstein models is one of the most commonly used model classes used for identifying nonlinear systems a static input nonlinearity followed by a linear dynamical part is an adequate way to model many real life systems this paper investigates the asymptotic in terms of sample size variance of hammerstein model estimates the work extends earlier results by ninness and gibson 2002 in the following ways not only frequency function estimation but estimation of general quantities is considered the expressions are not restricted to be valid asymptotically in the model order in addition the results cover model structures having noise models and allow for data generated under feedback the increase in variance due to the estimation of the input nonlinearity is characterized in particular under open loop operation white additive noise and the assumption of a separable process it is shown that the variance increase is exactly a term that was observed in ninness and gibson 2002 to result in good agreement with simulations this term vanishes in the formal asymptotic in model order analysis in ninness and gibson 2002

recursive identification of multi input multi output errors in variables hammerstein systems the note considers the identification of multi input multi output errors in variables hammerstein systems in which both the input and output can be observed but with additive noises being arma processes with unknown coefficients with the help of stochastic approximation combined with the deconvolution kernel function the recursive algorithms are proposed for estimating coefficients of the linear subsystem and for the values of the nonlinear function under some reasonable conditions all the estimates are proved to converge to the true values with probability one these results include identification of the errors in variables linear systems as a special case a simulation example is given justifying the theoretical analysis

desingularization of implicit analytic differential equations the question of finding solutions to a given implicit differential equation ide is an important one in part because it appears very naturally in several problems in physics engineering and many other fields in this work we show how to reduce a given analytic ide to an analytic ide of locally constant rank this can be done by using some fundamental results on subanalytic subsets and desingularization of closed subanalytic subsets an example from nonholonomic mechanics is studied using these methods

subspace identification of hammerstein systems using least squares support vector machines this paper presents a method for the identification of multiple input multiple output mimo hammerstein systems for the goal of prediction the method extends the numerical algorithms for subspace state space system identification n4sid mainly by rewriting the oblique projection in the n4sid algorithm as a set of componentwise least squares support vector machines ls svms regression problems the linear model and static nonlinearities follow from a low rank approximation of a matrix obtained from this regression problem

canonical desingularization in characteristic zero by blowing up the maximum strata of a local invariant this article contains an elementary constructive proof of resolution of singularities in characteristic zero our proof applies in particular to schemes of finite type and to analytic spaces so we recover the great theorems of hironaka we introduce a discrete local invariant rm inv x a whose maximum locus determines a smooth centre of blowing up leading to desingularization to define rm inv x we need only to work with a category of local ringed spaces x x cal o x satisfying certain natural conditions if a in x then rm inv x a depends only on widehat cal o x a more generally rm inv x is defined inductively after any sequence of blowings up whose centres have only normal crossings with respect to the exceptional divisors and lie in the constant loci of rm inv x cdot the paper is self contained and includes detailed examples one of our goals is that the reader understand the desingularization theorem rather than simply know it is true

on a question of pietsch about hilbert schmidt multilinear mappings abstract in 1983 pietsch asked if for n 3 and all hilbert spaces e1 en the vector space of the scalar valued absolutely r r1 rn summing multilinear mappings on e1 en coincides with the vector space of the n linear hilbert schmidt functionals on e1 en for some choice of r r1 rn 0 satisfying 1 r 1 r1 1 rn we show that the answer to this question is no moreover we show that the same question for n 2 and mappings with values in infinite dimensional hilbert spaces has the answer no

dynamic reconstruction of chaotic systems from inter spike intervals using least squares support vector machines this work presents a methodology for dynamic reconstruction of chaotic systems from inter spike interval isi time series obtained via integrate and fire if models in this methodology least squares support vector machines lssvms have been employed for approximating the dynamic behaviors of the systems under investigation higher generalization capability and avoidance of local minima constitute the main reasons behind the choice of lssvms as the approximation tool simulation results have shown that established lssvm models possess great potential for the reconstruction of chaotic dynamics in other words they are able to estimate some dynamic invariants of the underlying chaotic systems as well as they can accurately predict short term evolution within the horizon of predictability moreover lssvm models maintain their reconstruction performance even in the case of the existence of noisy data

local bifurcations and feasibility regions in differential algebraic systems the dynamics of a large class of physical systems such as the general power system can be represented by parameter dependent differential algebraic models of the form x spl dot f and 0 g typically such constrained models have singularities this paper analyzes the generic local bifurcations including those which are directly related to the singularity the notion of a feasibility region is introduced and analyzed it consists of all equilibrium states that can be reached quasistatically from the current operating point without loss of local stability it is shown that generically loss of stability at the feasibility boundary is caused by one of three different local bifurcations namely the saddle node and hopf bifurcations and a new bifurcation called the singularity induced bifurcation which is analyzed precisely here for the first time the latter results when an equilibrium point is at the singular surface under certain transversality conditions the change in the eigenstructure of the system jacobian at the equilibrium is established and the local dynamical structure of the trajectories near this bifurcation point is analyzed

summing multilinear operators by blocks the isotropic and anisotropic cases abstract unifying several directions of the development of the study of summing multilinear operators between banach spaces we construct a general framework that studies under one single definition multilinear operators that are summing with respect to sums taken over any number of indices iterated and non iterated sums the isotropic and the anisotropic cases sums over arbitrary blocks and over several different sequence norms a large number of special classes of multilinear operators and of methods of generating classes of multilinear operators are recovered as particular instances ideal properties and coincidence theorems for the general classes are proved

on the series expansion approach to the identification of hammerstein systems a polynomial identification algorithm for recovering a nonlinearity in the hammerstein system is proposed the estimate employs the legendre orthogonal system with adaptively selected number of terms the global consistency along with rates of convergence are established no assumptions concerning continuity of the nonlinearity or its functional form are made a data driven method using the cross validation technique for selecting the number of terms in the estimate is presented

a generalization of chetaev s principle for a class of higher order nonholonomic constraints the constraint distribution in nonholonomic mechanics has a double role on the one hand it is a kinematic constraint that is it is a restriction on the motion itself on the other hand it is also a restriction on the allowed variations when using d alembert s principle to derive the equations of motion we will show that many systems of physical interest where d alembert s principle does not apply can be conveniently modeled within the general idea of the principle of virtual work by the introduction of both kinematic constraints and variational constraints as being independent entities this includes for example elastic rolling bodies and pneumatic tires also d alembert s principle and chetaev s principle fall into this scheme we emphasize the geometric point of view avoiding the use of local coordinates which is the appropriate setting for dealing with questions of global nature like reduction

neural network approach for identification of hammerstein systems in this paper four different on line gradient based learning algorithms for training neural network hammerstein models are presented in a unified framework these algorithms namely the backpropagation for series parallel models the backpropagation sensitivity method and truncated backpropagation through time algorithm bptt for parallel models are derived analysed and compared for the truncated bptt it is shown that determination of the number of unfolding time steps necessary to calculate the gradient with an assumed degree of accuracy can be made on the basis of impulse response functions of sensitivity models the algorithms are shown to differ in their computational complexity gradient approximation accuracy and convergence rates numerical examples are also included to compare the performance of the algorithms

limitations of feedback properties imposed by open loop right half plane poles an integral constraint on the closed loop transfer function of unstable open loop systems is used to quantify a tradeoff between feedback properties of the closed loop system this results in a lower bound on the peak of the complementary sensitivity function for unstable plants the lower bound illustrates the nature of the tradeoff that is imposed and provides insight into the difficulties imposed on the control of unstable systems it can be used to quantitatively evaluate tradeoffs between the frequency of the open loop unstable pole the peak of the complementary sensitivity functions and the bandwidth of the closed loop system such a relationship has long been recognized and is embodied in the classical design rule of thumb which states that the closed loop bandwidth must be at least twice as large as the frequency of the unstable pole results which give insight into this rule and a theoretical basis for it are provided

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

identification of a class of nonlinear autoregressive models with exogenous inputs based on kernel machines in this paper we propose a new approach to identify a new class of nonlinear autoregressive models with exogenous inputs narx based on kernel machine and space projection kmsp the well known hammerstein wiener model which includes blocks of nonlinear static functions in series with a linear dynamic block is a subset of the narx models considered in the kmsp based approach kernel machine is used to represent the functions and space projection to separate the represented functions we also discuss two possible ambiguities and give conditions to avoid such ambiguities the asymptotic behavior of the proposed approach is analyzed the performance of the proposed method is verified by simulation studies

on cohen almost summing multilinear operators abstract we show that all cohen strongly p summing multilinear operators and polynomials on banach spaces are cohen almost summing if the range space is a hilbert space then all cohen strongly p summing multilinear operators and polynomials are strongly q summing 1 p q moreover if both domain and range spaces are hilbert spaces then all hilbert schmidt multilinear operators are cohen strongly 2 summing

nonholonomic mechanical systems with symmetry this work develops the geometry and dynamics of mechanical systems with nonholonomic constraints and symmetry from the perspective of lagrangian mechanics and with a view to control theoretical applications the basic methodology is that of geometric mechanics applied to the formulation of lagrange d alembert generalizing the use of connections and momentum maps associated with a given symmetry group to this case we begin by formulating the mechanics of nonholonomic systems using an ehresmann connection to model the constraints and show how the curvature of this connection enters into lagrange s equations unlike the situation with standard configuration space constraints the presence of symmetries in the nonholonomic case may or may not lead to conservation laws however the momentum map determined by the symmetry group still satisfies a useful differential equation that decouples from the group variables this momentum equation which plays an important role in control problems involves parallel transport operators and is computed explicitly in coordinates an alternative description using a body reference frame relates part of the momentum equation to the components of the euler poincar e equations along those symmetry directions consistent with the constraints one of the purposes of this paper is to derive this evolution equation for the momentum and to distinguish geometrically and mechanically the cases where it is conserved and those where it is not an example of the former is a ball or vertical disk rolling on a flat plane and an example of the latter is the snakeboard a modified version of the skateboard which uses momentum coupling for locomotion generation we construct a synthesis of the mechanical connection and the ehresmann connection defining the constraints obtaining an important new object we call the nonholonomic connection when the nonholonomic connection is a principal connection for the given symmetry group we show how to perform lagrangian reduction in the presence of nonholonomic constraints generalizing previous results which only held in special cases several detailed examples are given to illustrate the theory september 1994 revised march 1995 revised june 1995

tracking in nonlinear differential algebraic control systems with applications to constrained robot systems we consider the design of a feedback control law for control systems described by a class of nonlinear differential algebraic equations so that certain desired outputs track given reference inputs the nonlinear differential algebraic control system being considered is not in state variable form assumptions are introduced and a procedure is developed such that an equivalent state realization of the control system described by nonlinear differential algebraic equations is expressed in a familiar normal form a nonlinear feedback control law is then proposed which ensures under appropriate assumptions that the tracking error in the closed loop differential algebraic system approaches zero exponentially applications to simultaneous contact force and position tracking in constrained robot systems with rigid joints constrained robot systems with joint flexibility and constrained robot systems with significant actuator dynamics are discussed

brief paper revisiting hammerstein system identification through the two stage algorithm for bilinear parameter estimation the two stage algorithm tsa has been extensively used and adapted for the identification of hammerstein systems it is essentially based on a particular formulation of hammerstein systems in the form of bilinearly parameterized linear regressions this paper has been motivated by a somewhat contradictory fact though the optimality of the tsa has been established by bai in 1998 only in the case of some special weighting matrices the unweighted tsa is usually used in practice it is shown in this paper that the unweighted tsa indeed gives the optimal solution of the weighted nonlinear least squares problem formulated with a particular weighting matrix this provides a theoretical justification of the unweighted tsa and also leads to a generalization of the obtained result to the case of colored noise with noise whitening numerical examples of identification of hammerstein systems are presented to validate the theoretical analysis

nonlinear black box modeling in system identification a unified overview a nonlinear black box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics there has been considerable recent interest in this area with structures based on neural networks radial basis networks wavelet networks and hinging hyperplanes as well as wavelet transform based methods and models based on fuzzy sets and fuzzy rules this paper describes all these approaches in a common framework from a user s perspective it focuses on what are the common features in the different approaches the choices that have to be made and what considerations are relevant for a successful system identification application of these techniques it is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space these mappings are discussed separately the latter mapping is usually formed as a basis function expansion the basis functions are typically formed from one simple scalar function which is modified in terms of scale and location the expansion from the scalar argument to the regressor space is achieved by a radial or a ridge type approach basic techniques for estimating the parameters in the structures are criterion minimization as well as two step procedures where first the relevant basis functions are determined using data and then a linear least squares step to determine the coordinates of the function approximation a particular problem is to deal with the large number of potentially necessary parameters this is handled by making the number of used parameters considerably less than the number of offered parameters by regularization shrinking pruning or regressor selection

real analytic desingularization and subanalytic sets an elementary approach we give a proof of a theorem on desingularization of real analytic functions which is a weaker version of h hironaka s result but has the advantage of being completely self contained and elementary and not involving any machinery from algebraic geometry we show that the basic facts about subanalytic sets can be proved from this result

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