brief paper weighted least squares based recursive parametric identification for the submodels of a pwarx system a piecewise affine autoregressive system with exogenous inputs pwarx is composed of a finite number of arx subsystems each of which corresponds to a polyhedral partition of the regression space in this work a weighted least squares wls estimator is suggested to recursively estimate the parameters of the arx submodels in which a sequence of kernel functions are introduced conditions on the input signal and the pwarx system are imposed to guarantee the almost sure convergence of the wls estimates some numerical examples are included to illustrate performances of the algorithm

approximations of marginal tail probabilities and inference for scalar parameters summary in many situations inference for a scalar parameter in the presence of nuisance parameters requires integration of either a joint density of pivotal quantities or a joint posterior density for such inference accurate approximations of marginal tail probabilities are useful to avoid high dimensional integrals two tail probability approximations are developed in this paper numerical results given for conditional inference in location scale and linear regression models show the approximations to be generally accurate even for small sample sizes

a clustering technique for the identification of piecewise affine systems we propose a new technique for the identification of discrete time hybrid systems in the piecewise affine pwa form this problem can be formulated as the reconstruction of a possibly discontinuous pwa map with a multi dimensional domain in order to achieve our goal we provide an algorithm that exploits the combined use of clustering linear identification and pattern recognition techniques this allows to identify both the affine submodels and the polyhedral partition of the domain on which each submodel is valid avoiding gridding procedures moreover the clustering step used for classifying the datapoints is performed in a suitably defined feature space which allows also to reconstruct different submodels that share the same coefficients but are defined on different regions measures of confidence on the samples are introduced and exploited in order to improve the performance of both the clustering and the final linear regression procedure

transient electromagnetic inversion a remedy for magnetotelluric static shifts surficial bodies can severely distort magnetotelluric mt apparent resistivity data to arbitrarily low frequencies this distortion known as the mt static shift is due to an electric field generated from boundary charges on surficial inhomogeneities and persists throughout the entire mt recording range static shifts are manifested in the data as vertical parallel shifts of log log apparent resistivity sounding curves the impedance phase being unaffected using a three dimensional 3 d numerical modeling algorithm simulated mt data with finite length electrode arrays are generated significant static shifts are produced in this simulation however for some geometries they are impossible to identify techniques such as spatial averaging and electromagnetic array profiling emap are effective in removing static shifts but they are expensive especially for correcting a previously collected mt data set parametric representation and use of a single invariant quantity such as the impedance tensor

recursive identification for nonlinear arx systems based on stochastic approximation algorithm the nonparametric identification for nonlinear autoregressive systems with exogenous inputs narx described by yk 1 f yk yk 1 n0 uk uk 1 n0 ek 1 is considered first a condition on f is introduced to guarantee ergodicity and stationarity of yk then the kernel function based stochastic approximation algorithm with expanding truncations saawet is proposed to recursively estimate the value of f at any given y 1 y n0 u 1 u n0 r2n0 it is shown that the estimate converges to the true value with probability one in establishing the strong consistency of the estimate the properties of the markov chain associated with the narx system play an important role numerical examples are given which show that the simulation results are consistent with the theoretical analysis the intention of the paper is not only to present a concrete solution to the problem under consideration but also to profile a new analysis method for nonlinear systems the proposed method consisting in combining the markov chain properties with stochastic approximation algorithms may be of future potential although a restrictive condition has to be imposed on f that is the growth rate of f x should not be faster than linear with coefficient less than 1 as x tends to infinity

electromagnetic modeling of three dimensional bodies in layered earths using integral equations we have developed an algorithm based on the method of integral equations to simulate the electromagnetic responses of three dimensional bodies in layered earths the inhomogeneities are replaced by an equivalent current distribution which is approximated by pulse basis functions a matrix equation is constructed using the electric tensor green s function appropriate to a layered earth and it is solved for the vector current in each cell subsequently scattered fields are found by integrating electric and magnetic tensor green s functions over the scattering currents efficient evaluation of the tensor green s functions is a major consideration in reducing computation time we find that tabulation and interpolation of the six electric and five magnetic hankel transforms defining the secondary green s functions is preferable to any direct hankel transform calculation using linear filters a comparison of responses over elongate three dimensional 3 d bodies with responses over two dimensional 2 d bodie

a recursive local linear estimator for identification of nonlinear arx systems asymptotical convergence and applications in this paper we propose a recursive local linear estimator rlle for nonparametric identification of nonlinear autoregressive systems with exogenous inputs narx first the rlle is introduced next the strong consistency as well as the asymptotical mean square error properties of the rlle are established and then an application of the rlle to an additive nonlinear system is discussed the rlle provides recursive estimates not only for the function values but also their gradients at fixed points a simulation example is provided to confirm the theoretical analysis

a bounded error approach to piecewise affine system identification this paper proposes a three stage procedure for parametric identification of piecewise affine autoregressive exogenous pwarx models the first stage simultaneously classifies the data points and estimates the number of submodels and the corresponding parameters by solving the partition into a minimum number of feasible subsystems min pfs problem for a suitable set of linear complementary inequalities derived from data second a refinement procedure reduces misclassifications and improves parameter estimates the third stage determines a polyhedral partition of the regressor set via two class or multiclass linear separation techniques as a main feature the algorithm imposes that the identification error is bounded by a quantity spl delta such a bound is a useful tuning parameter to trade off between quality of fit and model complexity the performance of the proposed pwa system identification procedure is demonstrated via numerical examples and on experimental data from an electronic component placement process in a pick and place machine

adaptive control of weiner type nonlinear systems abstract this study is concerned with the problem of adaptive control of a wiener type nonlinear process it is assumed that the linear dynamic part of the process can be represented by a pulse transfer function of known order with stable inverse followed by a known time delay the memoryless nonlinearity is one to one on a compact set and belongs to one of two classes 1 piecewise linear with known breakpoints or 2 smooth function in which case an approximation is made it is represented by linear splines the output signal from the linear part is not available for measurement the global stability of the proposed model reference adaptive control scheme is established subject to the assumption that the nonlinearity can be represented exactly by the linear spline function with a given set of breakpoints otherwise there will be a residual tracking error which depends on the nonlinearity approximation error but the boundedness of all signals can be assured the proposed scheme is used for adaptive control of the ph process in a continuous flow reactor

the use and misuse of apparent resistivity in electromagnetic methods problems and misunderstandings arise with the concept of apparent resistivity when the analogy between an apparent resistivity computed from geophysical observations and the true resistivity structure of the subsurface is drawn too tightly several definitions of apparent resistivity are available for use in electromagnetic methods however those most commonly used do not always exhibit the best behavior many of the features of the apparent resistivity curve which have been interpreted as physically significant with one definition disappear when alternative definitions are used it is misleading to compare the detection or resolution capabilities of different field systems or configurations solely on the basis of the apparent resistivity curve for the in loop transient electromagnetic tem method apparent resistivity computed from the magnetic field response displays much better behavior than that computed from the induced voltage response a comparison of exact and asymptotic formulas for the te

recursive prediction error identification and scaling of non linear state space models using a restricted black box parameterization a recursive prediction error algorithm for identification of systems described by non linear ordinary differential equation ode models is presented the model is an ode model parameterized with coefficients of a multi variable polynomial that describes one component of the right hand side function of the ode this avoids over parameterization problems the selected model can also handle systems with more complicated right hand side structure by identification of a local input output equivalent system in the coordinate system of the selected state variables a novel technique based on scaling of the sampling period is proposed the technique can improve the conditioning of the identification problem thereby enhancing the chances of convergence to the correct minimum of the criterion the algorithm is applied to live data from a system consisting of two cascaded tanks with promising results a matlab software package which implements the proposed algorithm and a set of support scripts can be freely downloaded from http www it uu se research reports

identification of iir nonlinear systems without prior structural information in this paper we propose a kernel method for identification of infinite impulse response iir nonlinear systems without prior structural information the main result of the paper is to establish the conditions for asymptotic convergence in terms of the input to output exponential stability the performance of the method is tested on real world applications and computer simulations the results obtained show the efficacy of the method

assessing the accuracy of normal approximations abstract the widespread applicability and use of normal approximations creates a need for methods for assessing their accuracy in an operational fashion in this article two new methods are proposed and exemplified these new methods are based on a comparison of the level curves of an exact likelihood with the level curves of the usual normal approximation to it and on a comparison of selected line integrals of an exact density and a normal approximation to it the operational usefulness of these methods is compared with the operational usefulness of two well known existing approaches to assessing the accuracy of approximations namely convergence rates and the curvature methods of bates and watts 1980 as an example the second method is applied to the lee geisser 1972 1975 approximation to the predictive distribution of dental measurements on girls at the ages of 8 10 12 and 14 years in this application the method shows that the 50 central predictive probability region has actual predicti

bayesian marginal inference abstract a method is proposed for approximating the marginal posterior density of a continuous function of several unknown parameters thus permitting inferences about any parameter of interest for nonlinear models when the sample size is finite possibly tedious numerical integrations are replaced by conditional maximizations which are shown to be quite accurate in a number of special cases there are similarities with the profile likelihood ideas originated by kalbfleisch and sprott 1970 and the method is contrasted with a laplacian approximation recommended by kass tierney and kadane 1988 in press referred to here as the ktk procedure the methods are used to approximate the marginal posterior densities of the log linear interaction effects and an overall measure of association in a two way contingency table snee s 1974 hair eye color data are reanalyzed and adjustments are proposed to goodman s 1964 analysis for the full rank interaction model another application concerns marginaliz

diagnostic plots for improved parameterization in bayesian inference summary a major problem with the bayesian analysis of statistical models is that the computation of posterior and predictive summaries typically requires the numerical evaluation of complicated multivariate integrals the accuracy and efficiency of these computations are usually sensitive to the choice of parameterization for the statistical model this paper describes a graphical method which can be used both for assessing a proposed parameterization and choosing a new improved parameterization

identification of piecewise affine systems via mixed integer programming this paper addresses the problem of identification of hybrid dynamical systems by focusing the attention on hinging hyperplanes and wiener piecewise affine autoregressive exogenous models in which the regressor space is partitioned into polyhedra with affine submodels for each polyhedron in particular we provide algorithms based on mixed integer linear or quadratic programming which are guaranteed to converge to a global optimum for the special case where the estimation data only seldom switches between the different submodels we also suggest a way of trading off between optimality and complexity by using a change detection approach

transient electromagnetic response of a three dimensional body in a layered earth the three dimensional 3 d electromagnetic scattering problem is first formulated in the frequency domain in terms of an electric field volume integral equation three dimensional responses are then fourier transformed with sine and cosine digital filters or with the decay spectrum the digital filter technique is applied to a sparsely sampled frequency sounding which is replaced by a cubic spline interpolating function prior to convolution with the digital filters typically 20 to 40 frequencies at five to eight points per decade are required for an accurate solution a calculated transient is usually in error after it has decayed more than six orders in magnitude from early to late time the decay spectrum usually requires ten frequencies for a satisfactory solution however the solution using the decay spectrum appears to be less accurate than the solution using the digital filters particularly after early times checks on the 3 d solution include reciprocity and convergence checks in the frequenc

identification of piecewise affine systems based on statistical clustering technique this paper is concerned with the identification of a class of piecewise affine systems called a piecewise affine autoregressive exogenous pwarx model the pwarx model is composed of arx sub models each of which corresponds to a polyhedral region of the regression space under the temporary assumption that the number of sub models is known a priori the input output data are collected into several clusters by using a statistical clustering algorithm we utilize support vector classifiers to estimate the boundary hyperplane between two adjacent regions in the regression space in each cluster the parameter vector of the sub model is obtained by the least squares method it turns out that the present statistical clustering approach enables us to estimate the number of sub models based on the information criteria such as caic and mdl the estimate of the number of sub models is performed by applying the identification procedure several times to the same data set after having fixed the number of sub models to different values finally we verify the applicability of the present identification method through a numerical example of a hammerstein model

a mixed effects model for overdispersed count data in animal breeding count data models are developed for animal breeding applications to account for more variability than in a poisson mixed effects model a gamma distribution is assigned to poisson parameters thereby leading to a negative binomial model the natural log of the expected value of the poisson parameter is expressed as a linear function of fixed and random polygenic effects the negative binomial and poisson mixed models were compared in two simulations in the first marginal maximum likelihood mml estimates of genetic variances obtained under a poisson sire model psm and under a poisson animal model pam accounting for half sib relationships were different contrary to what occurs in a gaussian mixed linear model mml estimates of genetic variance under a negative binomial sire model were less biased than estimates under a psm and had a slightly smaller mean squared error mse the second simulation compared animal models in which the variance of the residuals was larger than the genetic variance empirical relative bias and mse of mml estimates of genetic variance were larger under a pam that ignored the residuals than under a negative binomial animal model differences in performance widened as genetic variance increased an application to the analysis of number of artificial inseminations until conception in dairy heifers is presented to illustrate potential differences in genetic variance estimates under the two animal models

accurate approximations for posterior moments and marginal densities abstract this article describes approximations to the posterior means and variances of positive functions of a real or vector valued parameter and to the marginal posterior densities of arbitrary i e not necessarily positive parameters these approximations can also be used to compute approximate predictive densities to apply the proposed method one only needs to be able to maximize slightly modified likelihood functions and to evaluate the observed information at the maxima nevertheless the resulting approximations are generally as accurate and in some cases more accurate than approximations based on third order expansions of the likelihood and requiring the evaluation of third derivatives the approximate marginal posterior densities behave very much like saddle point approximations for sampling distributions the principal regularity condition required is that the likelihood times prior be unimodal

on parameter transformations and interval estimation summary parameterizations which reduce the asymptotic bias and skewness of various pivotal quantities arising in large sample theory are discussed for models depending on an unknown scalar parameter transformation formulae by which such parameterizations can be obtained are derived and these formulae extend those for one dimensional curved exponential families given by hougaard 1982 to assess the accuracy of normal approximations to the distributions of the pivots their second order properties are considered and comparisons with the signed square root of the likelihood ratio statistic are drawn

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