Adaptive Filtering Under Minimum Mean p-Power Error Criterion
1st Edition
Published on 31. May 2024
388 pages
978-1-040-01592-6 (ISBN)
Description
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. 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.
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.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
42 Tables, black and white; 91 Line drawings, color; 23 Line drawings, black and white; 91 Illustrations, color; 23 Illustrations, black and white
ISBN-13
978-1-040-01592-6 (9781040015926)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
07/2024
CRC Press
Unfortunately, price unknown
Available (delivery time upon request)
Wentao Ma | Badong Chen
Adaptive Filtering Under Minimum Mean p-Power Error Criterion
Book
05/2024
1st Edition
Chapman & Hall/CRC
€242.90
Shipment within 10-20 days
Persons
Wentao Ma received a BSc in Mathematics and Applied Mathematics from Shannxi University of Technology in 2003, an MSc in Computing Mathematics from Huazhong University of Science and Technology in 2008, and a Ph.D. 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. He is a member of IEEE, IEEE PES, and CAA.
Badong Chen received BSc and MSc degrees in Control Theory and Engineering from Chongqing University in 1997 and 2003 respectively. He also received his Ph.D. 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 include signal processing, machine learning, artificial intelligence, neural engineering, and robotics. He has published four books and over 300 papers in various journals and conference proceedings. He is an IEEE Senior Member and has served as a Technical Committee Member of IEEE SPS Machine Learning for Signal Processing (MLSP). He has been 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. He has also been on the editorial board of Entropy.
Badong Chen received BSc and MSc degrees in Control Theory and Engineering from Chongqing University in 1997 and 2003 respectively. He also received his Ph.D. 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 include signal processing, machine learning, artificial intelligence, neural engineering, and robotics. He has published four books and over 300 papers in various journals and conference proceedings. He is an IEEE Senior Member and has served as a Technical Committee Member of IEEE SPS Machine Learning for Signal Processing (MLSP). He has been 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. He has also been on the editorial board of Entropy.
Content
1. Introduction 2. Adaptive Filtering Algorithms under MMSE Criterion 3. MMPE Family Criteria 4. Adaptive Filtering Algorithms under MMPE 5. Recursive Adaptive Filtering Algorithms under MMPE 6. Nonlinear Filtering Algorithms under MMPE 7. Adaptive Filtering Algorithms under Mixture MMPE 8. Adaptive Filtering Algorithms under KMPE Family Criteria
