Stochastic models, estimation and control. Volume 2 / Peter S. Maybeck.

Format:

Book

Author/Creator:

Maybeck, Peter S.

Series:

Mathematics in science and engineering ; v. 141.

Mathematics in science and engineering ; v. 141

Language:

English

Subjects (All):

Control theory.

Estimation theory.

System analysis.

Physical Description:

1 online resource (307 p.)

Place of Publication:

New York : Academic Press, 1982.

Language Note:

English

Summary:

Stochastic Models: Estimation and Control: v. 2

Contents:

Front Page; Stochastic Models, Estimation, and Control; Copyright Page; Contents; Preface; Notation; VOLUME 2; Chapter 8. Optimal smoothing; 8.1 Introduction; 8.2 Basic Structure; 8.3 Three Classes of Smoothing Problems; 8.4 Fixed-Interval Smoothing; 8.5 Fixed-Point Smoothing; 8.6 Fixed-Lag Smoothing; 8.7 Summary; References; Problems; Chapter 9. Compensation of linear model inadequacies; 9.1 Introduction; 9.2 Pseudonoise Addition and Artificial Lower Bounding of P; 9.3 Limiting Effective Filter Memory and Overweighting Most Recent Data; 9.4 Finite Memory Filtering

9.5 Linearized and Extended Kalman Filters9.6 Summary; References; Problems; Chapter 10. Parameter uncertainties and adaptive estimation; 10.1 Introduction; 10.2 Problem Formulation; 10.3 Uncertainties in F and Bd: Lkelihood Equations; 10.4 Uncertainties in F and Bd : Full-Scale Estimator; 10.5 Uncertainties in F and Bd : Performance Analysis; 10.6 Uncertainties in F and Bd : Attaining Online Applicability; 10.7 Uncertainties in Qd and R; 10.8 Bayesian and Multiple Model Filtering Algorithms; 10.9 Correlation Methods for Self-Tuning: Residual ""Whitening""

10.10 Covariance Matching and Other Techniques10.11 Summary; References; Problems; Chapter 11. Nonlinear stochastic system models; 11.1 Introduction; 11.2 Extensions of Linear System Modeling; 11.3 Markov Process Fundamentals; 11.4 Itô Stochastic Integrals and Differentials; 11.5 Itô Stochastic Differential Equations; 11.6 Forward Kolmogorov Equation; 11.7 Summary; References; Problems; Chapter 12. Nonlinear estimation; 12.1 Introduction; 12.2 Nonlinear Filtering with Discrete-Time Measurements: Conceptually; 12.3 Conditional Moment Estimators

12.4 Conditional Quasi-Moments and Hermite Polynomial Series12.5 Conditional Mode Estimators; 12.6 Statistically Linearized Filter; 12.7 Nonlinear Filtering with Continuous-Time Measurements; 12.8 Summary; References; Problems; Index

Notes:

Description based upon print version of record.

Includes bibliographies and index.

ISBN:

1-282-29029-0

9786612290299

0-08-095651-3

OCLC:

428099564