System Parameter Identification
Information Criteria and Algorithms
- 1st Edition - July 17, 2013
- Imprint: Elsevier
- Authors: Badong Chen, Yu Zhu, Jinchun Hu, Jose C. Principe
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 0 3 1 6 - 6
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 0 4 5 7 4 - 3
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 4 5 9 5 - 8
Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of… Read more
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Request a sales quoteRecently, 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
Engineers, scientists and graduate students interested in information theory, signal processing, system identification and adaptive system training.
- About the Authors
- Preface
- Symbols and Abbreviations
- 1. Introduction
- 1.1 Elements of System Identification
- 1.2 Traditional Identification Criteria
- 1.3 Information Theoretic Criteria
- 1.4 Organization of This Book
- Appendix A Unifying Framework of ITL
- 2. Information Measures
- 2.1 Entropy
- 2.2 Mutual Information
- 2.3 Information Divergence
- 2.4 Fisher Information
- 2.5 Information Rate
- Appendix B -Stable Distribution
- Appendix C Proof of (2.17)
- Appendix D Proof of Cramer–Rao Inequality
- 3. Information Theoretic Parameter Estimation
- 3.1 Traditional Methods for Parameter Estimation
- 3.2 Information Theoretic Approaches to Classical Estimation
- 3.3 Information Theoretic Approaches to Bayes Estimation
- 3.4 Information Criteria for Model Selection
- Appendix E: EM Algorithm
- Appendix F: Minimum MSE Estimation
- Appendix G: Derivation of AIC Criterion
- 4. System Identification Under Minimum Error Entropy Criteria
- 4.1 Brief Sketch of System Parameter Identification
- 4.2 MEE Identification Criterion
- 4.3 Identification Algorithms Under MEE Criterion
- 4.4 Convergence Analysis
- 4.5 Optimization of -Entropy Criterion
- 4.6 Survival Information Potential Criterion
- 4.7 Δ-Entropy Criterion
- 4.8 System Identification with MCC
- Appendix H Vector Gradient and Matrix Gradient
- 5. System Identification Under Information Divergence Criteria
- 5.1 Parameter Identifiability Under KLID Criterion
- 5.2 Minimum Information Divergence Identification with Reference PDF
- 6. System Identification Based on Mutual Information Criteria
- 6.1 System Identification Under the MinMI Criterion
- 6.2 System Identification Under the MaxMI Criterion
- Appendix I MinMI Rate Criterion
- References
- Edition: 1
- Published: July 17, 2013
- No. of pages (eBook): 266
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780128103166
- Hardback ISBN: 9780124045743
- eBook ISBN: 9780124045958
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Badong Chen
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.
Affiliations and expertise
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA and Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, ChinaYZ
Yu Zhu
Yu Zhu received the B.S. degree in radio electronics in 1983 from Beijing Normal University, and the M.S. degree in computer applications in 1993, and the Ph.D. degree in mechanical design and theory in 2001, both from China University of Mining and Technology. He is currently a professor with the Department of Mechanical Engineering, Tsinghua University. His research field mainly covers IC manufacturing equipment development strategy, ultra-precision air/maglev stage machinery design theory and technology, ultra-precision measurement theory and technology, and precision motion control theory and technology. Prof. Zhu has more than 140 research papers and 100 (48 awarded) invention patents.
Affiliations and expertise
Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, ChinaJH
Jinchun Hu
Jinchun Hu，associate professor, born in 1972, graduated from Nanjing University of Science & Technology. He received the B.Eng and Ph.D. degrees in control science and engineering in 1994 and 1998, respectively. Now he works at the Department of Mechanical Engineering, Tsinghua University. His current research interests include modern control theory and control systems, ultra-precision measurement principles and methods, micro/nano motion control system analysis and realization, special driver technology and device for precision motion systems, and super-precision measurement & control.
Affiliations and expertise
Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, ChinaJP
Jose C. Principe
Jose C. Principe is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs) modeling. He is BellSouth Professor and the Founding Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL). His primary research interests are in advanced signal processing with information theoretic criteria (entropy and mutual information) and adaptive models in reproducing kernel Hilbert spaces (RKHS), and the application of these advanced algorithms to Brain Machine Interfaces (BMI). Dr. Principe is a Fellow of the IEEE, ABME, and AIBME. He is the past Editor in Chief of the IEEE Transactions on Biomedical Engineering, past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, and Past-President of the International Neural Network Society. He received the IEEE EMBS Career Award, and the IEEE Neural Network Pioneer Award. He has more than 600 publications and 30 patents (awarded or filed).
Affiliations and expertise
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USARead System Parameter Identification on ScienceDirect