Overview
- Developments of the Total Least Square (TLS) algorithms for parameter estimation and adaptive filtering
- Reviews the basic TLS algorithms and derives novel method with detailed steps
- Provides detailed formula derivation of all the new methods and solid experiment verifications
Part of the book series: Engineering Applications of Computational Methods (EACM, volume 21)
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Keywords
- TLS
- Total Least Square
- Orthogonal Regression
- Neural-Based Orthogonal Regression
- the Errors-in-varianles Method
- Minor Component Analysis
About this book
Authors and Affiliations
About the authors
Da-Zheng Feng was born in December 1959. He received the Diploma degree from Xi’an University of Technology, Xi’an, China, in 1982, the M.S. degree from Xi’an Jiaotong University, Xi’an, China, in 1986, and the Ph.D. degree in electronic engineering from Xidian University, Xi’an, China, in 1995. From May 1996 to May 1998, he was a Postdoctoral Research Affiliate and an Associate Professor with Xi’an Jiaotong University, China. From May 1998 to June 2000, he was an Associate Professor with Xidian University. Since July 2000, he has been a Professor at Xidian University. His current research interests include signal processing, intelligence and brain information processing, and InSAR. He has published more than 150 journal papers, in which more than 60 articles were published in premier journal including IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems, and Neural Networks. He has been Principle Investigator of six grants from the National Natural Science Foundation of China.
Bibliographic Information
Book Title: Efficient Online Learning Algorithms for Total Least Square Problems
Authors: Xiangyu Kong, Dazheng Feng
Series Title: Engineering Applications of Computational Methods
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Science Press 2024
Hardcover ISBN: 978-981-97-1764-4Due: 27 May 2024
Softcover ISBN: 978-981-97-1767-5Due: 27 May 2024
eBook ISBN: 978-981-97-1765-1Due: 27 May 2024
Series ISSN: 2662-3366
Series E-ISSN: 2662-3374
Edition Number: 1
Number of Pages: XX, 227
Number of Illustrations: 39 b/w illustrations, 46 illustrations in colour
Additional Information: Jointly published with Science Press, Beijing, China