Titlebook: Efficient Online Learning Algorithms for Total Least Square Problems; Xiangyu Kong,Dazheng Feng Book 2024 Science Press 2024 TLS.Total Lea

Covenant

書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems影響因子(影響力)




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems影響因子(影響力)學(xué)科排名




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems被引頻次




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems被引頻次學(xué)科排名




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems年度引用




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems年度引用學(xué)科排名




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems讀者反饋




書(shū)目名稱Efficient Online Learning Algorithms for Total Least Square Problems讀者反饋學(xué)科排名

使無(wú)效

獨(dú)裁政府

DIS

無(wú)能力之人

2662-3366 derives novel method with detailed steps.Provides detailed 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 al

多山

多山

Metamorphosis

Total Least Square Problems, orthogonal data fitting-the EXIN neural networks. John Wiley & Sons Inc, Publication, 2010), who considered an approximate method for solving the matrix equation .?=?. when in both . and . there exist errors.

吹牛大王

Neural-Based TLS Algorithms,mputational complexity compared with other iterative methods, which make them more suitable in real-time application. There are three neural ways of solving TLS problem: (1) One is a neural network for the SVD, which finds the right singular vector associated with the smallest singular value of the

Cupidity

TLS Algorithm Under Non-Gaussian Noises,d that merely minimizes the sum of squared output error, the TLS method views the estimation problem as a fitting problem with noise existing in both input and output data and its goal is to minimize the sum of squared “total” perturbation/ error required to fit the input.