"…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
About the Author
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.
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.
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).