04312nam a22006015i 4500 978-981-97-1765-1 DE-He213 20251030112635.0 cr nn 008mamaa 240718s2024 si | s |||| 0|eng d 9789819717651 978-981-97-1765-1 10.1007/978-981-97-1765-1 doi TA342-343 PBWH bicssc TBJ bicssc MAT003000 bisacsh PBWH thema TBJ thema Kong, Xiangyu. author. aut http://id.loc.gov/vocabulary/relators/aut 72888 Efficient Online Learning Algorithms for Total Least Square Problems [electronic resource] / by Xiangyu Kong, Dazheng Feng. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XXVII, 269 p. 87 illus., 47 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Engineering Applications of Computational Methods, 2662-3374 ; 21 Introduction -- Least Square Problems -- Total Least Square Methods -- Fast Recursive TLS Algorithms -- Approximate Inverse Power Iteration TLS Algorithm -- Neural Based MCA Algorithms for Adaptive TLS -- Neural-Based SVD Algorithms -- Neural based TLS Algorithms -- TLS Algorithm Under Non-Gaussian Noises -- Performance Analysis Methods of TLS Algorithms. This book reports the developments of the Total Least Square (TLS) algorithms for parameter estimation and adaptive filtering. Specifically, it introduces the authors’ latest achievements in the past 20 years, including the recursive TLS algorithms, the approximate inverse power iteration TLS algorithm, the neural based MCA algorithm, the neural based SVD algorithm, the neural based TLS algorithm, the TLS algorithms under non-Gaussian noises, performance analysis methods of TLS algorithms, etc. In order to faster the understanding and mastering of the new methods provided in this book for readers, before presenting each new method in each chapter, a specialized section is provided to review the closely related several basis models. Throughout the book, large of procedure of new methods are provided, and all new algorithms or methods proposed by us are tested and verified by numerical simulations or actual engineering applications. Readers will find illustrative demonstration examples on a range of industrial processes to study. Readers will find out the present deficiency and recent developments of the TLS parameter estimation fields, and learn from the the authors’ latest achievements or new methods around the practical industrial needs. In my opinion, this book can be assimilated by advanced undergraduates and graduate students, as well as statisticians, because of the new tools in data analysis, applied mathematics experts, because of the novel theories and techniques that we propose, engineers, above all for the applications in control, system identification, computer vision, and signal processing. Mathematical models. 66910 Control engineering. Algorithms. 58677 Mathematics Data processing. 65715 Mathematical Modeling and Industrial Mathematics. 66912 Control and Systems Theory. 65868 Algorithms. 58677 Computational Science and Engineering. 65717 Feng, Dazheng. author. aut http://id.loc.gov/vocabulary/relators/aut 80800 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819717644 Printed edition: 9789819717668 Printed edition: 9789819717675 Engineering Applications of Computational Methods, 2662-3374 ; 21 72891 https://doi.org/10.1007/978-981-97-1765-1 ZDB-2-SMA ZDB-2-SXMS 61700 61700