- Print ISBN 9780124045743
- Electronic ISBN 9780124045958
Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors’ research provides a base for the book, but it incorporates the results from the latest international research publications.
Engineers, scientists and graduate students interested in information theory, signal processing, system identification and adaptive system training.
"…almost all of the variables used in the formulas are defined, something I cannot say about many other mathematical books…I found this book timely, interesting, and very well written. Readers can learn about estimation methodologies, the art of proof, and identification of the parameters assumed by the system architect or designer."--ComputingReviews.com, March 5, 2014
"Chen… Zhu, Hu…and Principe…synthesize their recent papers into a single-volume reference on system identification under criteria based on the information theory descriptors of entropy and dissimilarity. They cover information measures, information theoretic parameter estimation, system identification under minimum error entropy criteria, system identification under information divergence criteria, and system identification based on mutual information criteria."--Reference & Research Book News, December 2013