A comprehensive introduction to the most popular class of neural network, the multilayer perceptron, showing how it can be used for system identification and control. The book provides readers with a sufficient theoretical background to understand the characteristics of different methods, and to be aware of the pit-falls so as to make the correct decisions in all situations. This is a very application-oriented text that gives detailed and pragmatic recommendations to guide users through the plethora of methods suggested in the literature. Furthermore, it introduces sound working procedures that can lead to efficient neural network solutions. Invaluable to the practitioner and as a textbook in courses with a significant hands-on component.
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Etat : Gut. Zustand: Gut - Gebrauchs- und Lagerspuren. 1. Auflage. Außen: Kleiner Riss, verschmutzt. Innen: Seiten eingerissen, Seiten verschmutzt, Seiten vergilbt. | Seiten: 264 | Sprache: Englisch | Produktart: Bücher. N° de réf. du vendeur 694/3
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Paperback. Etat : Good. 2000. Corr. 3rd. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported. N° de réf. du vendeur 1852332271-11-1
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component. 264 pp. Englisch. N° de réf. du vendeur 9781852332273
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component. N° de réf. du vendeur 9781852332273
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. Neuware -The technology of neural networks has attracted much attention in recentyears. Their ability to learn nonlinear relationships is widelyappreciated and is utilized in many different types of applications;modelling of dynamic systems, signal processing, and control system designbeing some of the most common. The theory of neural computing has maturedconsiderably over the last decade and many problems of neural networkdesign, training and evaluation have been resolved. This book provides acomprehensive introduction to the most popular class of neural networkthe multilayer perceptron, and shows how it can be used for systemidentification and control. It aims to provide the reader with asufficient theoretical background to understand the characteristics ofdifferent methods, to be aware of the pit-falls and to make properdecisions in all situations. The subjects treated include:System identification: multilayer perceptrons; how to conduct informativeexperiments; model structure selection; training methods; modelvalidation; pruning algorithms.Control: direct inverse, internal model, feedforward, optimal andpredictive control; feedback linearization andinstantaneous-linearization-based controllers.Case studies: prediction of sunspot activity; modelling of a hydraulicactuator; control of a pneumatic servomechanism; water-level control in aconical tank.The book is very application-oriented and gives detailed and pragmaticrecommendations that guide the user through the plethora of methodssuggested in the literature. Furthermore, it attempts to introduce soundworking procedures that can lead to efficient neural network solutions.This will make the book invaluable to the practitioner and as a textbookin courses with a significant hands-on component.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 264 pp. Englisch. N° de réf. du vendeur 9781852332273
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Etat : New. Neural networks are of increasing interest to control engineersOf the several books available on this subject none is an advanced textbookA comprehensive introduction to the most popular class of neural network, the multilayer perceptron, showing ho. N° de réf. du vendeur 4289421
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 405. N° de réf. du vendeur C9781852332273
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