Applications neural adaptive control (13 resultados)

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies . , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.…

Taschenbuch. Condición: Neu. Adaptive Control with Recurrent High-order Neural Networks | Theory and Industrial Applications | George A. Rovithakis (u. a.) | Taschenbuch | Advances in Industrial Control | xii | Englisch | 2011 | Springer | EAN 9781447112013 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: Sehr gut. Zustand: Sehr gut | Seiten: 316 | Sprache: Englisch | Produktart: Bücher | This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9-10, 1996, in Berlin. The workshop was organised in connection with a three-year European-Union-funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland).The NACT project, which began on 1 April 1994, is a study of the fundamental properties of neural-network-based adaptive control systems. Where possible, links with traditional adaptive control systems are exploited. A major aim is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from within the Daimler-Benz group of companies.The aim of the workshop was to bring together selected invited specialists in the fields of adaptive control, nonlinear systems and neural networks. The first workshop (NACT I) took place in Glasgow in May 1995 and was mainly devoted to theoretical issues of neural adaptive control. Besides monitoring further development of theory, the NACT II workshop was focused on industrial applications and software tools. This context dictated the focus of the book and guided the editors in the choice of the papers and their subsequent reshaping into substantive book chapters. Thus, with the project having progressed into its applications stage, emphasis is put on the transfer of theory of neural adaptive engineering into industrial practice. The contributors are therefore both renowned academics and practitioners from major industrial users of neurocontrol.…

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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Design of nonlinear observers has received considerable attention since the early development of methods for state estimation. The most popular approach is the extended Kalman filter (EKF) that goes through significant degradation in the presence of unmodeled nonlinearities. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown parameters where no apriori information about the unknown parameters is available. While establishing global results, these approaches are only applicable to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observers in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. A novel approach to nonlinear state estimation, robust to unmodeled dynamics, is proposed from the perspective of augmenting an EKF with an NN based adaptive element.…