Isbn: 9783642096952 - discrete-time high order neural control: trained with kalman filtering: 112 (14 risultati)

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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Neural networks have become a well-established methodology as exempli ed by their applications to identi cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o ers a better suited tool to model and control of nonlinear systems. There exist di erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better tted for real-time implementations.…

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Taschenbuch. Condizione: Neu. Discrete-Time High Order Neural Control | Trained with Kalman Filtering | Edgar N. Sanchez (u. a.) | Taschenbuch | Studies in Computational Intelligence | x | Englisch | 2010 | Springer | EAN 9783642096952 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Neural networks have become a well-established methodology as exempli ed by their applications to identi cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o ers a better suited tool to model and control of nonlinear systems. There exist di erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better tted for real-time implementations. 120 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputsNeural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and con.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Neural networks have become a well-established methodology as exempli ed by their applications to identi cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o ers a better suited tool to model and control of nonlinear systems. There exist di erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better tted for real-time implementations.Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 120 pp. Englisch.…

Condizione: New. PRINT ON DEMAND pp. 120.