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Learning Augmented Recursive Estimation For Unceratin Non Linear Dynamic Systems
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Book Synopsis Learning Augmented Recursive Estimation for Unceratin Non-linear Dynamic Systems by : Stark Christiaan Draper
Download or read book Learning Augmented Recursive Estimation for Unceratin Non-linear Dynamic Systems written by Stark Christiaan Draper and published by . This book was released on 1996 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings, IEEE International Symposium on Intelligent Control by :
Download or read book Proceedings, IEEE International Symposium on Intelligent Control written by and published by . This book was released on 1996 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Recursive Estimation and Time-Series Analysis by : Peter C. Young
Download or read book Recursive Estimation and Time-Series Analysis written by Peter C. Young and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has grown out of a set of lecture notes prepared originally for a NATO Summer School on "The Theory and Practice of Systems ModelLing and Identification" held between the 17th and 28th July, 1972 at the Ecole Nationale Superieure de L'Aeronautique et de L'Espace. Since this time I have given similar lecture courses in the Control Division of the Engineering Department, University of Cambridge; Department of Mechanical Engineering, University of Western Australia; the University of Ghent, Belgium (during the time I held the IBM Visiting Chair in Simulation for the month of January, 1980), the Australian National University, and the Agricultural University, Wageningen, the Netherlands. As a result, I am grateful to all the reci pients of these lecture courses for their help in refining the book to its present form; it is still far from perfect but I hope that it will help the student to become acquainted with the interesting and practically useful concept of recursive estimation. Furthermore, I hope it will stimulate the reader to further study the theoretical aspects of the subject, which are not dealt with in detail in the present text. The book is primarily intended to provide an introductory set of lecture notes on the subject of recursive estimation to undergraduate/Masters students. However, the book can also be considered as a "theoretical background" handbook for use with the CAPTAIN Computer Package.
Book Synopsis Recursive Nonlinear Estimation by : Rudolph Kulhavy
Download or read book Recursive Nonlinear Estimation written by Rudolph Kulhavy and published by Springer. This book was released on 2014-03-12 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a close analogy to matching data in Euclidean space, this monograph views parameter estimation as matching of the empirical distribution of data with a model-based distribution. Using an appealing Pythagorean-like geometry of the empirical and model distributions, the book brings a new solution to the problem of recursive estimation of non-Gaussian and nonlinear models which can be regarded as a specific approximation of Bayesian estimation. The cases of independent observations and controlled dynamic systems are considered in parallel; the former case giving initial insight into the latter case which is of primary interest to the control community. A number of examples illustrate the key concepts and tools used. This unique monograph follows some previous results on the Pythagorean theory of estimation in the literature (e.g., Chentsov, Csiszar and Amari) but extends the results to the case of controlled dynamic systems.
Book Synopsis Masters Theses in the Pure and Applied Sciences Accepted by Colleges and Universities of the United States and Canada by :
Download or read book Masters Theses in the Pure and Applied Sciences Accepted by Colleges and Universities of the United States and Canada written by and published by . This book was released on 1996 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Recursive Nonlinear Estimation by : Rudolf Kulhavý
Download or read book Recursive Nonlinear Estimation written by Rudolf Kulhavý and published by . This book was released on 1996 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a close analogy to matching data in Euclidean space, this monograph views parameter estimation as matching of the empirical distribution of data with a model-based distribution. Using an appealing Pythagorean-like geometry of the empirical and model distributions, the book brings a new solution to the problem of recursive estimation of non-Gaussian and nonlinear models which can be regarded as a specific approximation of Bayesian estimation. The cases of independent observations and controlled dynamic systems are considered in parallel; the former case giving initial insight into the latter case which is of primary interest to the control community. A number of examples illustrate the key concepts and tools used. This unique monograph follows some previous results on the Pythagorean theory of estimation in the literature (e.g., Chentsov, Csiszar and Amari) but extends the results to the case of controlled dynamic systems.
Book Synopsis Recursive Parameter Estimation of Non-linear Dynamic Systems by : Enso Ikonen
Download or read book Recursive Parameter Estimation of Non-linear Dynamic Systems written by Enso Ikonen and published by . This book was released on 1993 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Recursive Estimation Algorithm for Identification of Dynamic Systems by : Toney R. Perkins
Download or read book A Recursive Estimation Algorithm for Identification of Dynamic Systems written by Toney R. Perkins and published by . This book was released on 1974 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: The report deals with the identification of unknown parameters in dynamic systems. In modeling a physical system, the problem of identifying the dynamics of a system are often encountered. The developed algorithm provides a tool to model all or parts of a dynamic system using input-output data sets from a real system. The methodology and techniques of this algorithm are based upon linear recursive estimation theory. The theoretical foundation and the pragmatics of using the ensemble data to estimate the unknown parameters are discussed at length in the development of the algorithm. As an application of the algorithm, experimental data from a man-in-the-loop simulation is used to estimate the parameters of a single axis model of the gunner. The tracking response of the gunner model compare favorably with data obtained from the simulation. (Modified author abstract).
Book Synopsis Model Validation and Uncertainty Quantification, Volume 3 by : Roland Platz
Download or read book Model Validation and Uncertainty Quantification, Volume 3 written by Roland Platz and published by Springer Nature. This book was released on 2023-10-06 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Introduction of Uncertainty Quantification Uncertainty Quantification in Dynamics Model Form Uncertainty and Selection incl. Round Robin Challenge Sensor and Information Fusion Virtual Sensing, Certification, and Real-Time Monitoring Surrogate Modeling
Book Synopsis A Recursive Estimation Algorithm for Discrete-time Systems with Unknown Noise Parameters by : Nathan Guedalia
Download or read book A Recursive Estimation Algorithm for Discrete-time Systems with Unknown Noise Parameters written by Nathan Guedalia and published by . This book was released on 1978 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this report is to develop a recursive algorithm for state estimation for systems with uncertain models. The algorithm uses a combined detection-estimation scheme whereby the set in which the uncertainties are contained is detected, and appropriate estimator is then used. The approach used is an extension of a weighted minimax performance criteria to the dynamic case. Since global optimal solution is not possible, an approximate algorithm is derived which only optimizes the stage-by-stage performance without changing any past decisions. The expressions for the algorithm and an approximation of its performance are derived. (Author).
Book Synopsis Optimal State Estimation by : Dan Simon
Download or read book Optimal State Estimation written by Dan Simon and published by John Wiley & Sons. This book was released on 2006-06-19 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
Book Synopsis International Aerospace Abstracts by :
Download or read book International Aerospace Abstracts written by and published by . This book was released on 1998 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Identification of Dynamic Systems by : Rolf Isermann
Download or read book Identification of Dynamic Systems written by Rolf Isermann and published by Springer. This book was released on 2011-04-08 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.
Book Synopsis Bayesian Filtering and Smoothing by : Simo Särkkä
Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
Book Synopsis Model-Based Processing by : James V. Candy
Download or read book Model-Based Processing written by James V. Candy and published by John Wiley & Sons. This book was released on 2019-03-15 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: A bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems Model-Based Processing: An Applied Subspace Identification Approach provides expert insight on developing models for designing model-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles—all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features: Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters Practical processor designs including comprehensive methods of performance analysis Provides a link between model development and practical applications in model-based signal processing Offers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications Enables readers to bridge the gap from statistical signal processing to subspace identification Includes appendices, problem sets, case studies, examples, and notes for MATLAB Model-Based Processing: An Applied Subspace Identification Approach is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia.
Book Synopsis Applied Nonlinear Control by : Jean-Jacques E. Slotine
Download or read book Applied Nonlinear Control written by Jean-Jacques E. Slotine and published by . This book was released on 1991 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, the authors present a global perspective on the methods available for analysis and design of non-linear control systems and detail specific applications. They provide a tutorial exposition of the major non-linear systems analysis techniques followed by a discussion of available non-linear design methods.
Book Synopsis Technology for Large Space Systems by :
Download or read book Technology for Large Space Systems written by and published by . This book was released on 1989 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt:
