Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time varying and unknown statistics environments. This application evolved communications, signal processing, seismology, mechanical design and control engineering. The most popular optimization criterion in adaptive filtering is the well-known minimum mean square error (MMSE) criterion, which is however only optimal when the signals involved are Gaussian distributed. Therefore, many “optimal solutions” under MMSE are not optimal actually. As an extension of the traditional MMSE, the minimum mean p-power error (MMPE) criterion has shown superior performance in many applications of adaptive filtering. This book aims to provide a comprehensive introduction of the MMPE and related adaptive filtering algorithms, which will become an important reference for researchers and practitioners in this application area. The book is geared to senior undergraduates with a basic understanding of linear algebra and statistics, graduate students, or practitioners with experience in adaptive signal processing.
Key Features:
Provides a systematic description of the MMPE criterion.
Many adaptive filtering algorithms under MMPE, including linear and nonlinear filters, will be introduced.
Extensive illustrative examples are included to demonstrate the results.
Author Biography:
Wentao Ma received B.S. degree in Mathematics and Applied Mathematics from Shannxi University of Technology, in 2003, M.S. degree in Computing Mathematics from Huazhong University of Science and Technology, in 2008, and Ph.D. degree in Information and Communication Engineering from Xi'an Jiaotong University, in 2015. Currently he is an associate professor with the School of Electrical Engineering, Xi'an University of Technology, Xi'an, China. His research interests include statistical signal processing, machine learning, artificial intelligence, and their applications in Electrical and Computer Engineering. He has published over 50 papers in various journals and conference proceedings. Dr. Ma is a member of IEEE, IEEE PES, and CAA.
Badong Chen received B.S. and M.S. degrees in Control Theory and Engineering from Chongqing University, in 1997 and 2003, and Ph.D. degree in Computer Science and Technology from Tsinghua University in 2008. He was a Postdoctoral Associate at the University of Florida Computational Neuro Engineering Laboratory (CNEL) from 2010 to 2012. He was a visiting research scientist at the Nanyang Technological University (NTU), Singapore in 2015. He also served as a senior research fellow with The Hong Kong Polytechnic University in 2017. Currently he is a professor at the Institute of Artificial Intelligence and Robotics (IAIR), Xi’an Jiaotong University. His research interests are in signal processing, machine learning, artificial intelligence, neural engineering and robotics. He has published 4 books and over 300 papers in various journals and conference proceedings, Dr. Chen is an IEEE Senior Member, and serves (or has served) as a Technical Committee Member of IEEE SPS Machine Learning for Signal Processing (MLSP), and an associate editor of IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Neural Networks and Learning Systems, Journal of The Franklin Institute and Neural Networks, and has been on the editorial board of Entropy.
