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020 _a9789819717651
_9978-981-97-1765-1
024 7 _a10.1007/978-981-97-1765-1
_2doi
050 4 _aTA342-343
072 7 _aPBWH
_2bicssc
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
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072 7 _aPBWH
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072 7 _aTBJ
_2thema
100 1 _aKong, Xiangyu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_972888
245 1 0 _aEfficient Online Learning Algorithms for Total Least Square Problems
_h[electronic resource] /
_cby Xiangyu Kong, Dazheng Feng.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXXVII, 269 p. 87 illus., 47 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aEngineering Applications of Computational Methods,
_x2662-3374 ;
_v21
505 0 _aIntroduction -- 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.
520 _aThis 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.
650 0 _aMathematical models.
_966910
650 0 _aControl engineering.
650 0 _aAlgorithms.
_958677
650 0 _aMathematics
_xData processing.
_965715
650 1 4 _aMathematical Modeling and Industrial Mathematics.
_966912
650 2 4 _aControl and Systems Theory.
_965868
650 2 4 _aAlgorithms.
_958677
650 2 4 _aComputational Science and Engineering.
_965717
700 1 _aFeng, Dazheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980800
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819717644
776 0 8 _iPrinted edition:
_z9789819717668
776 0 8 _iPrinted edition:
_z9789819717675
830 0 _aEngineering Applications of Computational Methods,
_x2662-3374 ;
_v21
_972891
856 4 0 _uhttps://doi.org/10.1007/978-981-97-1765-1
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c61700
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