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Recurrent Neural Networks: Design, Analysis, Applications to Control and Robotic Systems - Softcover
Inhaltsangabe
Because of massively parallel distributed nature and very fast convergence rates, recurrent neural networks (RNN) are widely applied to solving many problems in optimization, control and robotic systems, etc. Hence, this book investigates the following RNN models which solve some practical problems, together with their corresponding analysis on stability and convergence. A type of multilayer pole-assignment neural networks is applied to online synthesizing and tuning feedback control systems. Then, a novel RNN model is established by absorbing the first-order time-derivative information to solve the Sylvester equation with time-varying coefficient matrices. A dual neural network is developed to handle quadratic programs subject to linear constraints. The Lagrangian neural network and primal-dual neural network are also reviewed for comparison purposes. The neural networks are then exploited for real-time motion planning of redundant manipulators. The publication is primarily intended for researchers and postgraduates studying in RNN, control and robotics.
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Reseña del editor
Because of massively parallel distributed nature and very fast convergence rates, recurrent neural networks (RNN) are widely applied to solving many problems in optimization, control and robotic systems, etc. Hence, this book investigates the following RNN models which solve some practical problems, together with their corresponding analysis on stability and convergence. A type of multilayer pole-assignment neural networks is applied to online synthesizing and tuning feedback control systems. Then, a novel RNN model is established by absorbing the first-order time-derivative information to solve the Sylvester equation with time-varying coefficient matrices. A dual neural network is developed to handle quadratic programs subject to linear constraints. The Lagrangian neural network and primal-dual neural network are also reviewed for comparison purposes. The neural networks are then exploited for real-time motion planning of redundant manipulators. The publication is primarily intended for researchers and postgraduates studying in RNN, control and robotics.
Biografía del autor
Yunong Zhang is a professor at School of Information Science and Technology, Sun Yat-Sen University (SYSU), China. Before joining SYSU, he had been with National University of Ireland, University of Strathclyde, National University of Singapore, Chinese University of Hong Kong, since 1999. His main research interests are neural networks.
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Recurrent Neural Networks
Verlag:
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838303822
ISBN 13: 9783838303826
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Because of massively parallel distributed nature and very fast convergence rates, recurrent neural networks (RNN) are widely applied to solving many problems in optimization, control and robotic systems, etc. Hence, this book investigates the following RNN models which solve some practical problems, together with their corresponding analysis on stability and convergence. A type of multilayer pole-assignment neural networks is applied to online synthesizing and tuning feedback control systems. Then, a novel RNN model is established by absorbing the first-order time-derivative information to solve the Sylvester equation with time-varying coefficient matrices. A dual neural network is developed to handle quadratic programs subject to linear constraints. The Lagrangian neural network and primal-dual neural network are also reviewed for comparison purposes. The neural networks are then exploited for real-time motion planning of redundant manipulators. The publication is primarily intended for researchers and postgraduates studying in RNN, control and robotics. 200 pp. Englisch. Bestandsnummer des Verkäufers 9783838303826
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Recurrent Neural Networks
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
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ISBN 13: 9783838303826
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Recurrent Neural Networks
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838303822
ISBN 13: 9783838303826
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Recurrent Neural Networks
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LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838303822
ISBN 13: 9783838303826
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zhang YunongYunong Zhang is a professor at School of Information Science and Technology, Sun Yat-Sen University (SYSU), China. Before joining SYSU, he had been with National University of Ireland, University of Strathclyde, National . Bestandsnummer des Verkäufers 5411115
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Recurrent Neural Networks
Verlag:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2010
ISBN 10: 3838303822
ISBN 13: 9783838303826
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Recurrent Neural Networks
Verlag:
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838303822
ISBN 13: 9783838303826
Neu
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Because of massively parallel distributed nature and very fast convergence rates, recurrent neural networks (RNN) are widely applied to solving many problems in optimization, control and robotic systems, etc. Hence, this book investigates the following RNN models which solve some practical problems, together with their corresponding analysis on stability and convergence. A type of multilayer pole-assignment neural networks is applied to online synthesizing and tuning feedback control systems. Then, a novel RNN model is established by absorbing the first-order time-derivative information to solve the Sylvester equation with time-varying coefficient matrices. A dual neural network is developed to handle quadratic programs subject to linear constraints. The Lagrangian neural network and primal-dual neural network are also reviewed for comparison purposes. The neural networks are then exploited for real-time motion planning of redundant manipulators. The publication is primarily intended for researchers and postgraduates studying in RNN, control and robotics.Books on Demand GmbH, Überseering 33, 22297 Hamburg 200 pp. Englisch. Bestandsnummer des Verkäufers 9783838303826
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Recurrent Neural Networks : Design, Analysis, Applications to Control and Robotic Systems
Verlag:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838303822
ISBN 13: 9783838303826
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Because of massively parallel distributed nature and very fast convergence rates, recurrent neural networks (RNN) are widely applied to solving many problems in optimization, control and robotic systems, etc. Hence, this book investigates the following RNN models which solve some practical problems, together with their corresponding analysis on stability and convergence. A type of multilayer pole-assignment neural networks is applied to online synthesizing and tuning feedback control systems. Then, a novel RNN model is established by absorbing the first-order time-derivative information to solve the Sylvester equation with time-varying coefficient matrices. A dual neural network is developed to handle quadratic programs subject to linear constraints. The Lagrangian neural network and primal-dual neural network are also reviewed for comparison purposes. The neural networks are then exploited for real-time motion planning of redundant manipulators. The publication is primarily intended for researchers and postgraduates studying in RNN, control and robotics. Bestandsnummer des Verkäufers 9783838303826
Anzahl: 1 verfügbar
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Recurrent Neural Networks: Design, Analysis, Applications to Control and Robotic Systems
Verlag:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838303822
ISBN 13: 9783838303826
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