Identifiability of State Space Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Identifiability of State Space Models write by E. Walter. This book was released on 2013-03-07. Identifiability of State Space Models available in PDF, EPUB and Kindle.
Competition and Cooperation in Neural Nets
Competition and Cooperation in Neural Nets - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Competition and Cooperation in Neural Nets write by Eckart Frehland. This book was released on 1982. Competition and Cooperation in Neural Nets available in PDF, EPUB and Kindle.
Parameter Redundancy and Identifiability
Parameter Redundancy and Identifiability - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Parameter Redundancy and Identifiability write by Diana Cole. This book was released on 2020-05-10. Parameter Redundancy and Identifiability available in PDF, EPUB and Kindle. Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be possible to find parameter estimates for every parameter in that model, but, in some cases, this is not possible. For example, two parameters that only ever appear in the model as a product could not be estimated individually; only the product can be estimated. Such a model is said to be parameter redundant, or the parameters are described as non-identifiable. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context. Key features of this book: Detailed discussion of the problems caused by parameter redundancy and non-identifiability Explanation of the different general methods for detecting parameter redundancy and non-identifiability, including symbolic algebra and numerical methods Chapter on Bayesian identifiability Throughout illustrative examples are used to clearly demonstrate each problem and method. Maple and R code are available for these examples More in-depth focus on the areas of discrete and continuous state-space models and ecological statistics, including methods that have been specifically developed for each of these areas This book is designed to make parameter redundancy and non-identifiability accessible and understandable to a wide audience from masters and PhD students to researchers, from mathematicians and statisticians to practitioners using mathematical or statistical models.
Subspace Identification for Linear Systems
Subspace Identification for Linear Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Subspace Identification for Linear Systems write by Peter van Overschee. This book was released on 2012-12-06. Subspace Identification for Linear Systems available in PDF, EPUB and Kindle. Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering.
Blind Identification of Structured Dynamic Systems
Blind Identification of Structured Dynamic Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Blind Identification of Structured Dynamic Systems write by Chengpu Yu. This book was released on 2021-11-22. Blind Identification of Structured Dynamic Systems available in PDF, EPUB and Kindle. This book is intended for researchers active in the field of (blind) system identification and aims to provide new identification ideas/insights for dealing with challenging system identification problems. It presents a comprehensive overview of the state-of-the-art in the area, which would save a lot of time and avoid collecting the scattered information from research papers, reports and unpublished work. Besides, it is a self-contained book by including essential algebraic, system and optimization theories, which can help graduate students enter the amazing blind system identification world with less effort.
