Badong Chen, Yu Zhu, Jinchun Hu, Jose C. Principe

System Parameter Identification (eBook, ePUB)

Information Criteria and Algorithms

• Format: ePub

Produktbeschreibung

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.

• Named a 2013 Notable Computer Book for Information Systems by Computing Reviews
• One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developments
• Contains numerous illustrative examples to help the reader grasp basic methods

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 266
• Erscheinungstermin: 17. Juli 2013
• Englisch
• ISBN-13: 9780124045958
• Artikelnr.: 39504737

Autorenporträt

Badong Chen received the B.S. and M.S. degrees in control theory and engineering from Chongqing University, in 1997 and 2003, respectively, and the Ph.D. degree in computer science and technology from Tsinghua University in 2008. He was a Post-Doctoral Researcher with Tsinghua University from 2008 to 2010, and a Post-Doctoral Associate at the University of Florida Computational NeuroEngineering Laboratory (CNEL) during the period October, 2010 to September, 2012. He is currently a professor at the Institute of Artificial Intelligence and Robotics (IAIR), Xi'an Jiaotong University. His research interests are in system identification and control, information theory, machine learning, and their applications in cognition and neuroscience.