Now Publishers Inc Sparsity Methods for Systems and Control (NowOpen)

Product Description The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently, the sparsity method has been applied to systems and control to design resource-aware control systems. This book gives a comprehensive guide to sparsity methods for systems and control, from standard sparsity methods in finite-dimensional vector spaces (Part I) to optimal control methods in infinite-dimensional function spaces (Part II).The primary objective of this book is to show how to use sparsity methods for several engineering problems. For this, the author provides MATLAB programs by which the reader can try sparsity methods for themselves. Readers will obtain a deep understanding of sparsity methods by running these MATLAB programs.Sparsity Methods for Systems and Control is suitable for graduate level university courses, though it should also be comprehendible to undergraduate students who have a basic knowledge of linear algebra and elementary calculus. Also, especially part II of the book should appeal to professional researchers and engineers who are interested in applying sparsity methods to systems and control. Review Definitely this book will be interesting to broad control, mathematics, and engineering communities. The author should definitely try to expand the book to include sparse control problems in their full generality. -Aleksandar Haber, The City University of New York, USA--Aleksandar Haber (5/1/2020 12:00:00 AM)The writing style is live and easy to follow. In particular, I like very much the examples, problems, and Matlab simulations with actual code segments that are included in the first chapters. The proposal is timely and the topic of sparsity in control is very broad. In addition, to the systems and control community, I expect it will appeal to the machine learning community. - Ivan Markovsky, Vrije Universiteit Brussels, Belgium--Ivan Markovsky (4/15/2020 12:00:00 AM) About the Author Dr. Masaaki Nagahara is currently a full professor at Institute of Environmental Science and Technology, University of Kitakyushu, Japan. He is also a visiting professor at Indian Institute of Technology (IIT) Bombay, India, from 2017. His research interests include optimal control, cyber-physical systems, artificial intelligence, signal processing, and machine learning. He received Transition to Practice Award from IEEE Control Systems Society in 2012. He is a Senior Member of IEEE.