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.
Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
Derived from the renowned multi-volume International Encyclopaedia of Laws, this practical analysis of the law of contracts in Singapore covers every aspect of the subject – definition and classification of contracts, contractual liability, relation to the law of property, good faith, burden of proof, defects, penalty clauses, arbitration clauses, remedies in case of non-performance, damages, power of attorney, and much more. Lawyers who handle transnational contracts will appreciate the explanation of fundamental differences in terminology, application, and procedure from one legal system to another, as well as the international aspects of contract law. Throughout the book, the treatment emphasizes drafting considerations. An introduction in which contracts are defined and contrasted to torts, quasi-contracts, and property is followed by a discussion of the concepts of ‘consideration’ or ‘cause’ and other underlying principles of the formation of contract. Subsequent chapters cover the doctrines of ‘relative effect’, termination of contract, and remedies for non-performance. The second part of the book, recognizing the need to categorize an agreement as a specific contract in order to determine the rules which apply to it, describes the nature of agency, sale, lease, building contracts, and other types of contract. Facts are presented in such a way that readers who are unfamiliar with specific terms and concepts in varying contexts will fully grasp their meaning and significance. Its succinct yet scholarly nature, as well as the practical quality of the information it provides, make this book a valuable time-saving tool for business and legal professionals alike. Lawyers representing parties with interests in Singapore will welcome this very useful guide, and academics and researchers will appreciate its value in the study of comparative contract law.
Has the neuromuscular junction been over-exposed or is it perhaps already a closed book? I asked myself this at a recent International Congress when an American colleague complained that the Journal of Physiology had articles on nothing but the neuromuscular junction, while another colleague asked why I was editing a volume on a subject about which everything was already known. It is worrying to think that these views may be shared by other people. I hope that this volume will convince my two colleagues and other readers that the neuromuscular junction is very much alive and continues to attract the interest of many workers from a variety of fields; strange as it may seem, the synapse between a motor nerve ending and muscle fibre, with its relatively simple architecture, is one of the most inter esting sites in the body-I do hope we have done it justice. The various chapters of this volume present a cross section of knowledge as viewed by a group of 13 individuals, actively engaged in research. Multi-author volumes such as this are frequently criticised on the grounds that chapters or sec tions overlap. I believe that such criticium is only valid where the overlap is repetitious. Where it results in the reader having available discussions of material from differing stand-points, overlap becomes a valuable feature of this type of publication.
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.
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