Efficient Online Learning Algorithms For Total Least Square Problems
Download Efficient Online Learning Algorithms For Total Least Square Problems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Efficient Online Learning Algorithms For Total Least Square Problems book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Efficient Online Learning Algorithms for Total Least Square Problems
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.
Data-Driven Approaches for Efficient Smart Grid Systems
This Research Topic aims to highlight the exciting potential of innovative forecasting methods and their practical applications using machine learning in smart grid systems (SGSs). Machine learning techniques, which encompass traditional neural networks and advanced deep learning methods, have gained significant attention for their ability to address the complex challenges within SGSs and simultaneously improve cost-effectiveness. It's important to note that when machine learning models are employed in SGSs, they primarily focus on forecasting. This emphasis is grounded in the models' impressive capability to accurately replicate the intricate dynamics that characterize smart grid systems. By harnessing these forecasting models, researchers and practitioners are equipped with a valuable tool to better understand and predict the behavior of SGSs. This not only contributes to academic advancements but also enhances the practical implementation of smart grid technologies.
Twin Support Vector Machines
This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.