Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracjing

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【簡介】 The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/tracking Bayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear problems for which analytic evaluation of the exact performance is intractable. A widely used technique is to find bounds on the performance of any estimator and compare the performance of various estimators to these bounds. This book provides a comprehensive overview of the state of the art in Bayesian Bounds. It addresses two related problems: the estimation of multiple parameters based on noisy measurements and the estimation of random processes, either continuous or discrete, based on noisy measurements. An extensive introductory chapter provides an overview of Bayesian estimation and the interrelationship and applicability of the various Bayesian Bounds for both static parameters and random processes. It provides the context for the collection of papers that are included. This book will serve as a comprehensive reference for engineers and statisticians interested in both theory and application. It is also suitable as a text for a graduate seminar or as a supplementary reference for an estimation theory course. 【目錄】

Introduction to Nonlinear Control: Stability, Control Design, and Estimation 2023

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An introductory text on the analysis, control, and estimation of nonlinear systems, appropriate for advanced undergraduate and graduate students This self-contained and accessible introduction to the concepts and techniques used for nonlinear feedback systems offers a holistic treatment suitable for use in both advanced undergraduate and graduate courses; students need only some familiarity with differential equations and linear algebra to understand the material presented. The text begins with an overview of stability and Lyapunov methods for nonlinear systems, with Lyapunov’s second method revisited throughout the book as a connective thread. Other introductory chapters cover linear systems, frequency domain methods, and discrete-time systems. Building on this background material, the book provides a broad introduction to the basic ideas underpinning major themes of research in nonlinear control, including input-to-state stability, sliding mode control, adaptive control, feedback linearization, and robust output regulation. Chapters also cover observer design and estimation for nonlinear systems. The text is notable for its coverage of nonlinear model predictive control and its introduction to the use of linear matrix inequalities and semidefinite programming coupled with their use in modern antiwindup designs. • First text on nonlinear control appropriate for undergraduates • Suitable both for students preparing for rigorous graduate study and for those entering technical fields outside of academia • Unique in its coverage of recent research topics • Pedagogical features including extensive chapter summaries, examples, and appendixes with definitions, results, and MATLAB applications

Linear and Nonlinear Optimization using Spreadsheets (1版)

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【簡介】 The use of spreadsheets to obtain solutions to a diverse array of examples offers a reader-friendly way of addressing a topic (optimization) that can sometimes be viewed as intimidating. Many people are readily familiar with spreadsheets and how they work, yet are apt to be unaware of the incredible power of Excel for solving some rather complex optimization problems. A major goal of the book is to sell readers on why it is so important to understand optimization, and a large collection of examples for a wide range of business decision making areas (e.g., production planning and scheduling, workforce planning and scheduling, location and supply chain distribution, location of emergency services, assembly line balancing, vehicle routing, project scheduling, revenue management, advertising, product design, payout schedules, productivity measurement, investment portfolio management, sports league scheduling, ranking models, etc.) affords a practical mechanism for achieving that goal. Another important contribution of the book is that it provides coverage of the mechanics of some common yet sophisticated statistical methods (regression, logistic regression, discriminant analysis, factor analysis, and cluster analysis), which are often opaque to many users of such methods. 【目錄】 Introduction Optimization Examples in Prescriptive Analytics: Production Planning Workforce Planning Continuous Facility Location Discrete Facility Location Routing Problems Facility Layout Project Scheduling Marketing Finance Sports Optimization Examples for Multivariate Statistical Methods Used in Predictive and Descriptive Analytics: Regression Logistic Regression Linear Discriminant Analysis Factor Analysis Cluster Analysis