Download or read book Generalized Residual Multiple Model Adaptive Estimation of Parameters and States written by Charles D. Ormsby and published by . This book was released on 2003-10 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops a modification to the standard Multiple Model Adaptive Estimator (MMAE) which allows the use of a new "generalized residual" in the hypothesis conditional probability calculation. The generalized residual is a linear combination of traditional Kalman filter residuals and "post-fit" Kalman filter residuals which are calculated after measurement incorporation. This modified MMAE is termed a Generalized Residual Multiple Model Adaptive Estimator (GRMMAE). The dissertation provides a derivation of the hypothesis conditional probability formula which the GRMMAE uses to calculate probabilities that each elemental filter in the GRMMAE contains the correct parameter value. Through appropriate choice of a single scalar GRMMAE design parameter, the GRMMAE can be designed to be equivalent to a traditional MMAE, a post-fit residual modified MMAE, or any number of yet unused MMAEs. The original GRMMAE design goal was to choose the GRMMAE design parameter that caused the fastest GRMMAE convergence to the correct hypothesis. However, this dissertation demonstrates that the GRMMAE design parameter can lead to beta-dominance, a negative performance effect in the GRMMAE. This is a key contribution of this research as other researchers have previously suggested that the use of post-fit residuals may be advantageous in certain MMAE applications. This dissertation directly addresses the use of post-fit residuals by those researchers and demonstrates that, for their application, equivalent performance is achieved using a traditional MMAE. (10 tables, 27 figures, 47 refs.)

Download or read book Generalized Multiple Model Adaptive Estimation written by Badr N. Alsuwaidan and published by . This book was released on 2008 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: ?Pub Inc In this dissertation a generalized multiple-model adaptive estimator (GMMAE) is presented that can be used to estimate the unknown noise statistics in filter designs. The assumed unknowns in the adaptive estimator are the process noise covariance elements. Multiple parameter elements are used to drive multiple-model parallel filters for state estimation. The current approach focuses on estimating the process noise covariance by sequentially updating weights associated with parameter elements through the calculation of the likelihood function of the measurement-minusestimate residuals, which also incorporates correlations between various measurement times. For linear Gaussian measurement processes the likelihood function is easily determined. For nonlinear Gaussian measurement processes, it is assumed that the linearized output sufficiently captures the statistics of the likelihood function by making the small noise assumption. A proof is provided that shows the convergence properties of the generalized approach versus the traditional multiple-model adaptive estimator (MMAE). Simulation results, involving a two-dimensional target tracking problem ans GPS-based position estimation problem using an extended Kalman filter, indicate that the new approach is able to correctly estimate the noise statistics.

Download or read book New Variations of Multiple Model Adaptive Estimation for Improved Tracking and Identification written by Christopher K. Nebelecky and published by . This book was released on 2013 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple model adaptive estimation (MMAE) is a recursive algorithm that uses a bank of estimators, each purposefully dependent on a particular hypothesis, to determine an estimate of an uncertain system under consideration while simultaneously tracking the system state. The first generation of MMAE, introduced by Magill in 1965 considered the estimators to act independently and in parallel, determining state estimates conditional with each hypothesis. Through computation of a normalized mode-conditioned likelihood, the conditional probability that each hypothesis correctly models the system is computed. Since Magill's seminal work, many offshoots of MMAE have been developed. Modifications have been reported, but are typically on on an application specific basis which limits their versatility. In this dissertation, two variations of MMAE are considered. The first variation is based on an observed flaw which leads to degenerate tracking performance. The second variation is motivated by previous research which showed improved convergence performance by considering a generalized mode-conditioned likelihood function for determining the hypothesis conditional probabilities. Each estimator, or specifically Kalman filter, is designed around a particular system hypothesis. If the hypothesis is not sufficiently close to the true system, the resulting filter will generally produce erroneous estimates which do not track the system. This is because each filter believes that the hypothesized system is optimal. Further, the state error covariances resulting from such a suboptimal filter will be inconsistent because they have no knowledge of the incorrect hypothesized model. By explicitly accounting for the deviation of the hypothesis, recursions are developed which, when combined with MMAE are shown to provide superior tracking performance over the standard MMAE. Additionally the proposed variation, called model error MMAE, is shown to provide acceptable tracking performance for dynamically switching systems at a fraction of the computational expense of other algorithms specifically developed for that application. The second variation, referred to as generalized multiple model adaptive estimation (GMMAE), uses an augmented vector of current and past residuals to drive the recursion for the hypothesis conditional probabilities. Necessary for that recursion is evaluation of the time-domain autocovariance matrix of the residual sequence. When filtering linear (and linearized) systems, the autocovariance can be analytically expressed as a function of the system matrices, covariances and filter gain. When filtering nonlinear systems using the Unscented filter, analytic expressions for the autocovariance are not possible.^Motivated to include Unscented filters within the GMMAE framework, a method for calculating the time-domain autocovariance of the residual sequence from an Unscented filter is presented. The proposed method is validated analytically on a simplified system and simulation results are presented using the algorithm for process noise estimation in a planar tracking problem.

Download or read book Multivariate Generalized Linear Mixed Models Using R written by Damon Mark Berridge and published by CRC Press. This book was released on 2011-04-25 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples. Improve Your Longitudinal Study In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.

Download or read book An Optimal Parameter Discretization Strategy for Multiple Model Adaptive Estimation and Control written by Stuart N. Sheldon and published by . This book was released on 1989 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method is proposed for designing multiple model adaptive estimators (MMAE) to provide combined state and parameter estimation in the presence of an uncertain parameter vector. It is assumed that the parameter varies over a continuous region and a finite number of constant-gain filters is available for the estimation. The estimator elemental filters are chosen by minimizing a cost functional representing the average state prediction error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. A second-order example is used to illustrate the increase in performance over previously accepted filter selection methods. By minimizing a cost functional representing the average parameter prediction error autocorrelation, a parameter estimator is designed which is different from the state estimator. The parameter estimator found with this method provides lower average mean square parameter estimation error than previously accepted design methods. An analogous method is proposed for designing multiple model adaptive controllers to provide stabilizing control in the presence of an uncertain parameter vector. A finite number of constant-gain controllers is used to regulate a system with a parameter vector that varies over a continuous region of the parameter space. The controller elemental filters are chosen by minimizing a cost functional representing the average regulation error autocorrelation, with the average taken as the true parameter ranges over the admissible parameter set. Keywords: Numerical optimization, Adaptive control systems, Theses. (aw).

Download or read book A Generalized Residuals Model for the Unified Matrix Polynomial Approach to Frequency Domain Modal Parameter Estimation written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this dissertation is twofold: to generalize residuals for frequency domain modal parameter estimation and to introduce the notion of a consistency diagram for residuals. Residuals are simplified expressions included in an FRF model to account for the influence of out-of-band modes. The development of most frequency domain algorithms have had some consideration of residuals, but only as fixed set of terms. A consistency diagram, which is a widely popular technique in modal parameter estimation, has historically been generated by varying only the number of poles in the model. However, there are other conditions of the parameter estimation process, such as the residuals, that can be varied to produce consistent estimates of the system poles. An overview of the residual models and applications illustrates the need for a more systematic approach. The limitations of the physical residual model are established, which verifies that a more generalized residual model is justified. A power polynomial of frequency, with positive and negative orders, is proposed, developed and implemented as a generalized residual model for frequency domain modal parameter estimation. It is demonstrated that a polynomial function is able to sufficiently describe the off-resonance frequency response characteristics. The generalized.

Download or read book Adaptive Estimation of Random Parameter Regression Models by State Space Representation Methods written by Kiseok Lee and published by . This book was released on 1990 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Control and Estimation written by Robert F. Stengel and published by Courier Corporation. This book was released on 2012-10-16 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graduate-level text provides introduction to optimal control theory for stochastic systems, emphasizing application of basic concepts to real problems. "Invaluable as a reference for those already familiar with the subject." — Automatica.

Download or read book Adaptive Estimation and Control written by Keigo Watanabe and published by . This book was released on 1991 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unifies the partitioned adaptive estimators for stochastic systems and applies them to other estimation and control problems. The techniques, not restricted to lumped-parameter systems with unknown constant parameters, serve as a starting point for more complicated problems.

Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 860 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Adaptive Estimation in Time Series Regression Models written by Douglas Gardiner Steigerwald and published by . This book was released on 1989 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multiple Model Adaptive Estimation Using Filter Spawning written by Kenneth A. Fisher (2LT, USAF.) and published by . This book was released on 1999 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Model Based Fault Diagnosis for Dynamic Systems written by Jie Chen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an increasing demand for dynamic systems to become more safe and reliable. This requirement extends beyond the normally accepted safety-critical systems of nuclear reactors and aircraft where safety is paramount important, to systems such as autonomous vehicles and fast railways where the system availability is vital. It is clear that fault diagnosis (including fault detection and isolation, FDI) has been becoming an important subject in modern control theory and practice. For example, the number of papers on FDI presented in many control-related conferences has been increasing steadily. The subject of fault detection and isolation continues to mature to an established field of research in control engineering. A large amount of knowledge on model-based fault diagnosis has been ac cumulated through the literature since the beginning of the 1970s. However, publications are scattered over many papers and a few edited books. Up to the end of 1997, there is no any book which presents the subject in an unified framework. The consequence of this is the lack of "common language", dif ferent researchers use different terminology. This problem has obstructed the progress of model-based FDI techniques and has been causing great concern in research community. Many survey papers have been published to tackle this problem. However, a book which presents the materials in a unified format and provides a comprehensive foundation of model-based FDI is urgently needed.

Download or read book Application of Multiple Model Adaptive Estimation Algorithms to Maneuver Detection and Estimation written by J. Korn and published by . This book was released on 1983 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Item Response Theory written by Wim J. van der Linden and published by CRC Press. This book was released on 2018-02-19 with total page 1688 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing on the work of 75 internationally acclaimed experts in the field, Handbook of Item Response Theory, Three-Volume Set presents all major item response models, classical and modern statistical tools used in item response theory (IRT), and major areas of applications of IRT in educational and psychological testing, medical diagnosis of patient-reported outcomes, and marketing research. It also covers CRAN packages, WinBUGS, Bilog MG, Multilog, Parscale, IRTPRO, Mplus, GLLAMM, Latent Gold, and numerous other software tools. A full update of editor Wim J. van der Linden and Ronald K. Hambleton’s classic Handbook of Modern Item Response Theory, this handbook has been expanded from 28 chapters to 85 chapters in three volumes. The three volumes are thoroughly edited and cross-referenced, with uniform notation, format, and pedagogical principles across all chapters. Each chapter is self-contained and deals with the latest developments in IRT.

Download or read book Multiple Model Adaptive Estimation Applied to the LAMBDA URV for Failure Detection and Identification written by David W. Lane (CAPT, USAF.) and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: