Showing papers in "IEEE Transactions on Systems, Man, and Cybernetics in 1982"

Optimal Filtering

Abstract: Estimation theory has had a tremendous impact on many problem areas over the past two decades. Beginning with its original use in the aerospace industry, its applications can now be found in many different areas such as control and communjcations, power systems, transportation systems, bioengineering, image processing, etc. Along with linear system theory and optimal control, a course in estimation theorycan be found in the graduate system and control curriculum,of most schools in the country. In fact, it is probably one of the most,salable courses as far as employment is concerned. However, despite its economic value and the amount of activities in the field, very few books on estimation theory have appeared recently. This book helps to fill the void in the market and does that in a superb manner. Although the book is called OptimalFiltering, the coverage is restricted to discrete time filtering. A more appropriate title would thus be Optimal Discrete Time ,Filtering. The authors’ decision to concentrate on discrete time f lters is due to “recent technological developments as well as the easier path offered students and instructors.” This is probably a wise move since a thorough treatment of continuous time filtering will require a better knowledge o f stochastic processes than most graduate students or engineers will have. As it stands now, the text requires little background beyond that of linear system theory and probability theory. Written by active researchers, in the area, the book covers the standard topics such as Kalman filtering, innovations processes, smoothing, and adaptive and nonlinear estimation. Much of the material in the book has been around for a long time and has been widely used, by practitioners in the area: Some results are more recent. However,-it .has been difficult to locate all of them presented in a n organized manner within a single text. This is especially true of the chapters dealing with the computation aspects and nonlinear and adaptive estimation. After a short introductory chapter, Chapter 2 introduces the mathematical model to be used throughout most of the book. The discrete time Kalman filter is 1 hen presented in Chapter 3, along with some applications. Chapter 4 contains a treatment

Optimisation [by D.M. Greig (London: Longman, 1980, 179 pp.)]

Abstract: This book provides a compact survey of selected topics in NLP at an ideal level for advanced undergraduates, especially engineering students. There are a number of fine books on NLP but, to our knowledge, no text designed specifically for a one-semester course is quite as nice. The book consists of five chapters. In Chapter 1, the classical theory of optimization including convexity, Lagrange multipliers, and the Kuhn- Tucker conditions is presented. Chapter 2 examines methods for finding a minimum in the unconstrained case. Chapter 3 studies the theory behind linear programming and, of course, the simplex method. In Chapter 4, applications of linear programming such as the transportation problem, an allocation problem, and game theory are discussed. Finally, in Chapter 5, methods for solving constrained optimization problems are presented; projection methods, quadratic programming methods, penalty and barrier function methods, and Lagrangian methods are described in detail. The book is terse and suffers from a lack of illustrative examples. The instructor should planon providing motivation in order to overcome this limitation.