FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title Feedback Systems: An Introduction for Scientists and Engineers
 Authors Karl Johan Astrom, Richard M. Murray
 Publisher: Princeton University Press; illustrated edition edition (April 1, 2008)
 Paperback: 424 pages
 eBook: PDF (408 pages) and PDF Files
 Language: English
 ISBN10: 0691135762
 ISBN13: 9780691135762
 Share This:
Book Description
This book provides an introduction to the mathematics needed to model, analyze, and design feedback systems. It is an ideal textbook for undergraduate and graduate students, and is indispensable for researchers seeking a selfcontained reference on control theory. Unlike most books on the subject, Feedback Systems develops transfer functions through the exponential response of a system, and is accessible across a range of disciplines that utilize feedback in physical, biological, information, and economic systems.
Authors use techniques from physics, computer science, and operations research to introduce controloriented modeling. They begin with state space tools for analysis and design, including stability of solutions, Lyapunov functions, reachability, state feedback observability, and estimators. The matrix exponential plays a central role in the analysis of linear control systems, allowing a concise development of many of the key concepts for this class of models. Authors then develop and explain tools in the frequency domain, including transfer functions, Nyquist analysis, PID control, frequency domain design, and robustness. They provide exercises at the end of every chapter, and an accompanying electronic solutions manual is available. Feedback Systems is a complete onevolume resource for students and researchers in mathematics, engineering, and the sciences.
 Covers the mathematics needed to model, analyze, and design feedback systems
 Serves as an introductory textbook for students and a selfcontained resource for researchers
 Includes exercises at the end of every chapter
 Features an electronic solutions manual
 Offers techniques applicable across a range of disciplines
 Karl Johan Astrom is professor of automatic control at the Lund Institute of Technology in Sweden. His books include "Adaptive Control".
 Richard M. Murray is professor of control and dynamical systems at the California Institute of Technology. He is the coauthor of "A Mathematical Introduction to Robotic Manipulation".
 Operations Research (OR), Linear Programming, Optimization, Approximation
 Control Theory and Systems
 Applied Mathematics
 Numerical Analysis and Computation
 Financial Engineering and Financial Mathematics

Feedback Control Theory (John C. Doyle, et al)
An excellent introduction to feedback control system design, this book offers a theoretical approach that captures the essential issues and can be applied to a wide range of practical problems.

Call Center Optimization: Understanding and Improving
This book gives an overview of the role and potential of mathematical optimization in call centers. It deals extensively with all aspects of workforce management, but also with topics such as call routing and the scheduling of multiple channels.

Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

Machine Learning Yearning (Andrew Ng)
You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

Understanding Machine Learning: From Theory to Algorithms
Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

Reinforcement Learning: An Introduction, Second Edition
It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

Probabilistic Machine Learning: An Introduction (Kevin Murphy)
This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudocode for the most important algorithms.
:






















