This book reports the developments of the Total Least Square (TLS) algorithms for parameter estimation and adaptive filtering. Specifically, this book 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 by 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 (PH.D.) 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.

Focuses on the quality-related process monitoring of different situations, with MSPC-based models such as PCA, PLS Reviews the basic model and derives novel method with detailed steps Provides detailed formula derivation and solid experiment validation

Autorentext

Xiangyu Kong received the B.S. degree in optical engineering from Beijing Institute of Technology, P. R. China, in 1990, the M.S. degree in mechanical and electrical engineering from Xi'an Institute of Hi-Tech, in 2000, and the Ph.D. degree in automation science and engineering from Xi'an Jiaotong University, P. R. China, in 2005. He is currently a professor in the Department of Control Engineering of Xi'an Institute of Hi-Tech. His research interests include adaptive signal processing, neural networks and feature extraction, process monitoring, and fault diagnosis. He has published six monographs (all first author), including an English monograph published by Springer, and more than 150 papers, in which more than 40 articles were published in premier journals including IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems, and Neural Networks. He has been PIs of four grants from the National Natural Science Foundation of China. Jiayu Luo received Bachelor's degree from Hunan University, Hunan, China, in 2017, and Master's degree from Xi'an Institute of Hi-Tech., Xi'an, China, in 2019. He is currently pursuing Ph.D. degree in Xi'an Institute of Hi-Tech. He has published one book and 11 articles in IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Signal Processing, and other journals. His research interests include feature extraction, complex process monitoring, and fault diagnosis. Xiaowei Feng received his Bachelor's, Master's, and Ph.D. degrees from Xi'an Institute of High Tech., Xi'an, in 2008, 2011, and 2016, respectively. Now, he is working as a lecturer at Xi'an Institute of Hi-Tech. He has authored or co-authored more than 20 journal papers on IEEE Transactions on Signal Processing, IEEE Transactions on Neural Networks and Learning Systems,and other journals and has published 1 monograph. His research interests include feature extraction, industrial process monitoring, and fault diagnosis.

Inhalt
Chapter 1 Introduction.- Chapter 2 An Overview of Conventional MSPC Methods.- Chapter 3 System-wide Process Monitoring and Fault Diagnosis.- Chapter 4 Quality-Related Time-Varying Process Monitoring.- Chapter 5 Quality-Related Dynamic Process Monitoring: Part I.- Chapter 6 Quality-Related Dynamic Process Monitoring: Part II.- Chapter 7 Quality-Related Complex Nonlinear Process Monitoring.- Chapter 8 Quality-Related Fault Subspace Extraction for Fault Diagnosis.- Chapter 9 Non-Gaussian Process Monitoring and Fault Diagnosis.- Chapter 10 Hybrid Gaussian/Non-Gaussian Quality-Related Nonlinear Process Monitoring.- Chapter 11 Conclusions and Future Work.