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This monograph opens up new horizons for engineers and researchers in
academia and in industry dealing with or interested in new developments in the
field of system identification and control. It emphasizes guidelines for
working solutions and practical advice for their implementation rather than th...
This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential dif...
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learn...
This thesis introduces several new Gaussian process approximations based on the sparse spectrum approximation and the sparse Nyström approximation. We use several new variational Bayesian distribution techniques to extend the sparse spectrum approximation and combine these new extensions with the o...
This monograph opens up new horizons for engineers and researchers in
academia and in industry dealing with or interested in new developments in the
field of system identification and control. It emphasizes guidelines for
working solutions and practical advice for their implementation rather than th...
This monograph opens up new horizons for engineers and researchers in
academia and in industry dealing with or interested in new developments in the
field of system identification and control. It emphasizes guidelines for
working solutions and practical advice for their implementation rather than th...
This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential dif...
This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential dif...
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learn...
This book is intended as a guide to the analysis and presentation of experimental results. It develops various techniques for the numerical processing of experimental data, using basic statistical methods and the theory of errors. After presenting basic theoretical concepts, the book describes the m...
This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, ...
Image analysis is a fundamental task for extracting information from images acquired across a range of different devices. Since reliable quantitative results are requested, image analysis requires highly sophisticated numerical and analytical methods—particularly for applications in medicine, secu...