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Maximum Likelihood Prediction And Estimation For Nonlinear Dynamic Systems
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Book Synopsis Maximum-likelihood Prediction and Estimation for Nonlinear Dynamic Systems by : L. D. Attaway
Download or read book Maximum-likelihood Prediction and Estimation for Nonlinear Dynamic Systems written by L. D. Attaway and published by . This book was released on 1968 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: A method is given for determining the system state using noise-corrupted observations of a non-linear dynamic invector process, with a numerical application to radar observation of a reentry body. The study examined the feasibility of numerically solving the vector-differential equations satisfied by the maximum-likelihood estimator. The maximum-likelihood estimate is that initial condition which minimizes a certain functional on itself, on the observation, and on the a priori statistics.
Book Synopsis A Maximum Likelihood Approach to Prediction and Estimation for Nonlinear Dynamic Systems by : Leland Dalton Attaway
Download or read book A Maximum Likelihood Approach to Prediction and Estimation for Nonlinear Dynamic Systems written by Leland Dalton Attaway and published by . This book was released on 1968 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sequential Estimation for Discrete-time Nonlinear Systems by : J. S. Meditch
Download or read book Sequential Estimation for Discrete-time Nonlinear Systems written by J. S. Meditch and published by . This book was released on 1969 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of state and parameter estimation for noisy discrete-time nonlinear dynamic systems is examined from the viewpoint of marginal maximum likelihood estimation. Approximate algorithms for sequential prediction, filtering, and smoothing are developed. The former two are in agreement with previous results; the latter is new. A technique for iterative-sequential filtering and smoothing using Newton's method is indicated. A numerical example is included to illustrate the results. (Author).
Book Synopsis Modelling and Parameter Estimation of Dynamic Systems by : J.R. Raol
Download or read book Modelling and Parameter Estimation of Dynamic Systems written by J.R. Raol and published by IET. This book was released on 2004-08-13 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.
Book Synopsis Parameter Estimation in Nonlinear Dynamic Systems by : W. J. H. Stortelder
Download or read book Parameter Estimation in Nonlinear Dynamic Systems written by W. J. H. Stortelder and published by . This book was released on 1998 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum-likelihood parameter estimation for general non-linear dynamic systems by :
Download or read book Maximum-likelihood parameter estimation for general non-linear dynamic systems written by and published by . This book was released on 1987 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter estimation in nonlinear dynamical systems by : Walter Johannes Henricus Stortelder
Download or read book Parameter estimation in nonlinear dynamical systems written by Walter Johannes Henricus Stortelder and published by . This book was released on 1998 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Deterministic Sampling for Nonlinear Dynamic State Estimation by : Gilitschenski, Igor
Download or read book Deterministic Sampling for Nonlinear Dynamic State Estimation written by Gilitschenski, Igor and published by KIT Scientific Publishing. This book was released on 2016-04-19 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.
Author :A. Taylor Publisher :Createspace Independent Publishing Platform ISBN 13 :9781979722759 Total Pages :226 pages Book Rating :4.7/5 (227 download)
Book Synopsis System Identification With Matlab. Create Linear and Nonlinear Dynamic System Models by : A. Taylor
Download or read book System Identification With Matlab. Create Linear and Nonlinear Dynamic System Models written by A. Taylor and published by Createspace Independent Publishing Platform. This book was released on 2017-11-14 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: System Identification Toolbox provides MATLAB functions, Simulink blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use models of dynamic systems not easily modeled from first principles or specifications. You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation. The toolbox provides identification techniques such as maximum likelihood, prediction-error minimization (PEM), and subspace system identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting. The most important content that this book provides are the following: - System Identification Overview - What Is System Identification? - About Dynamic Systems and Models - System Identification Requires Measured Data - Building Models from Data - Black-Box Modeling - Grey-Box Modeling - Evaluating Model Quality - When to Use the App vs. the Command Line - System Identification Workflow - Commands for Model Estimation - Linear Model Identification - Identify Linear Models Using System Identification App - Preparing Data for System Identification - Saving the Session - Estimating Linear Models Using Quick Start - Estimating Linear Models - Viewing Model Parameters - Exporting the Model to the MATLAB Workspace - Exporting the Model to the Linear System Analyzer - Identify Linear Models Using the Command Line - Preparing Data - Estimating Impulse Response Models - Estimating Delays in the Multiple-Input System - Estimating Model Orders Using an ARX Model Structure - Estimating Transfer Functions - Estimating Process Models - Estimating Black-Box Polynomial Models - Simulating and Predicting Model Output - Identify Low-Order Transfer Functions (Process Models) - Using System Identification App - What Is a Continuous-Time Process Model? - Preparing Data for System Identification - Estimating a Second-Order Transfer Function (Process Model) - with Complex Poles - Estimating a Process Model with a Noise Component - Viewing Model Parameters - Exporting the Model to the MATLAB Workspace - Simulating a System Identification Toolbox Model in Simulink Software - Estimating Models Using Frequency-Domain Data - Advantages of Using Frequency-Domain Data - Representing Frequency-Domain Data in the Toolbox - Preprocessing Frequency-Domain Data for Model - Estimation - Estimating Linear Parametric Models - Validating Estimated Model - Next Steps After Identifying a Model - Nonlinear Model Identification - Identify Nonlinear Black-Box Models Using System - Identification App - What Are Nonlinear Black-Box Models? - Preparing Data - Estimating Nonlinear ARX Models - Estimating Hammerstein-Wiener Models
Book Synopsis Scientific and Technical Aerospace Reports by :
Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation Techniques for Nonlinear Dynamic Models with Limited Data, Process Disturbances and Modeling Errors by : Hadiseh Karimi
Download or read book Parameter Estimation Techniques for Nonlinear Dynamic Models with Limited Data, Process Disturbances and Modeling Errors written by Hadiseh Karimi and published by . This book was released on 2013 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis appropriate statistical methods to overcome two types of problems that occur during parameter estimation in chemical engineering systems are studied. The first problem is having too many parameters to estimate from limited available data, assuming that the model structure is correct, while the second problem involves estimating unmeasured disturbances, assuming that enough data are available for parameter estimation. In the first part of this thesis, a model is developed to predict rates of undesirable reactions during the finishing stage of nylon 66 production. This model has too many parameters to estimate (56 unknown parameters) and not having enough data to reliably estimating all of the parameters. Statistical techniques are used to determine that 43 of 56 parameters should be estimated. The proposed model matches the data well. In the second part of this thesis, techniques are proposed for estimating parameters in Stochastic Differential Equations (SDEs). SDEs are fundamental dynamic models that take into account process disturbances and model mismatch. Three new approximate maximum likelihood methods are developed for estimating parameters in SDE models. First, an Approximate Expectation Maximization (AEM) algorithm is developed for estimating model parameters and process disturbance intensities when measurement noise variance is known. Then, a Fully-Laplace Approximation Expectation Maximization (FLAEM) algorithm is proposed for simultaneous estimation of model parameters, process disturbance intensities and measurement noise variances in nonlinear SDEs. Finally, a Laplace Approximation Maximum Likelihood Estimation (LAMLE) algorithm is developed for estimating measurement noise variances along with model parameters and disturbance intensities in nonlinear SDEs. The effectiveness of the proposed algorithms is compared with a maximum-likelihood based method. For the CSTR examples studied, the proposed algorithms provide more accurate estimates for the parameters. Additionally, it is shown that the performance of LAMLE is superior to the performance of FLAEM. SDE models and associated parameter estimates obtained using the proposed techniques will help engineers who implement on-line state estimation and process monitoring schemes.
Book Synopsis Optimal Estimation of Dynamic Systems by : John L. Crassidis
Download or read book Optimal Estimation of Dynamic Systems written by John L. Crassidis and published by CRC Press. This book was released on 2004-04-27 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most newcomers to the field of linear stochastic estimation go through a difficult process in understanding and applying the theory.This book minimizes the process while introducing the fundamentals of optimal estimation. Optimal Estimation of Dynamic Systems explores topics that are important in the field of control where the signals receiv
Book Synopsis Maximum Likelihood Estimation of Nonlinear Systems of Equations by : William A. Barnett
Download or read book Maximum Likelihood Estimation of Nonlinear Systems of Equations written by William A. Barnett and published by . This book was released on 1974 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonlinear Dynamics and Statistics by : Alistair I. Mees
Download or read book Nonlinear Dynamics and Statistics written by Alistair I. Mees and published by Springer Science & Business Media. This book was released on 2001-01-25 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the state of the art in nonlinear dynamical reconstruction theory. The chapters are based upon a workshop held at the Isaac Newton Institute, Cambridge University, UK, in late 1998. The book's chapters present theory and methods topics by leading researchers in applied and theoretical nonlinear dynamics, statistics, probability, and systems theory. Features and topics: * disentangling uncertainty and error: the predictability of nonlinear systems * achieving good nonlinear models * delay reconstructions: dynamics vs. statistics * introduction to Monte Carlo Methods for Bayesian Data Analysis * latest results in extracting dynamical behavior via Markov Models * data compression, dynamics and stationarity Professionals, researchers, and advanced graduates in nonlinear dynamics, probability, optimization, and systems theory will find the book a useful resource and guide to current developments in the subject.
Book Synopsis Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models by : Ray C. Fair
Download or read book Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models written by Ray C. Fair and published by . This book was released on 1980 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: A solution method and an estimation method for nonlinear rational expectations models are presented in this paper. The solution method can be used in forecasting and policy applications and can handle models with serial correlation and multiple viewpoint dates. When applied to linear models, the solution method yields the same results as those obtained from currently available methods that are designed specifically for linear models. It is, however, more flexible and general than these methods. For large nonlinear models the results in this paper indicate that the method works quite well. The estimation method is based on the maximum likelihood principal. It is, as far as we know, the only method available for obtaining maximum likelihood estimates for nonlinear rational expectations models. The method has the advantage of being applicable to a wide range of models, including, as a special case, linear , models. The method can also handle different assumptions about the expectations of the exogenous variables, something which is not true of currently available approaches to linear models.
Book Synopsis Maximum Likelihood Estimation and Inference by : Russell B. Millar
Download or read book Maximum Likelihood Estimation and Inference written by Russell B. Millar and published by John Wiley & Sons. This book was released on 2011-07-26 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
Author :Kendall T. Publisher :Createspace Independent Publishing Platform ISBN 13 :9781539772279 Total Pages :222 pages Book Rating :4.7/5 (722 download)
Book Synopsis Basic System Identification with MATLAB by : Kendall T.
Download or read book Basic System Identification with MATLAB written by Kendall T. and published by Createspace Independent Publishing Platform. This book was released on 2016-10-27 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: System Identification Toolbox constructs mathematical models of dynamic systems from measured input-output data. It provides MATLAB(r) functions, Simulink blocks, and an interactive tool for creating and using models of dynamic systems not easily modeled from first principles or specifications You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process odels, and state-space models. The toolbox provides maximum likelihood, prediction-error minimization (PEM), subspace system identification, and other identification techniques.For nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for prediction of system response and for simulation in Simulink. The toolbox also lets you model time-series data and perform time-series forecasting. The more important content in this book is the next:* Transfer function, process model, and state-space model identification using time-domain and frequency-domain response data* Autoregressive (ARX, ARMAX), Box-Jenkins, and Output-Error model estimation using maximum likelihood, prediction-error minimization(PEM), and subspace system identification techniques * Time-series modeling (AR, ARMA, ARIMA) and forecasting* Identification of nonlinear ARX models and Hammerstein-Weiner models with input-output nonlinearities such as saturation and dead zone* Linear and nonlinear grey-box system identification for estimation of user-defined models* Delay estimation, detrending, filtering, resampling, and reconstruction of missing data
