neural networks modelling control (24 Ergebnisse)

Zustand: Good. Ships from the UK. Former library book; may include library markings. Used book that is in clean, average condition without any missing pages.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: Very Good. Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,500grams, ISBN:9781441951588.

paperbound, 8vo xvii+524 pp., ills. this book brings together examples of all the most important paradigms for the application of neural networks to robotics and control; neurocomputing; very good condition (unused).

Zustand: Very Good. Very Good condition. Good dust jacket. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Paperback. Zustand: Brand New. 165 pages. 9.25x6.25x0.75 inches. In Stock.

Paperback. Zustand: Brand New. 468 pages. 9.00x6.00x1.07 inches. In Stock.

Zustand: Gut. Zustand: Gut | Seiten: 144 | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In this monograph, new structures of neural networks in multidimensional domains are introduced. These architectures are a generalization of the Multi-layer Perceptron (MLP) in Complex, Vectorial and Hypercomplex algebra. The approximation capabilities of these networks and their learning algorithms are discussed in a multidimensional context. The work includes the theoretical basis to address the properties of such structures and the advantages introduced in system modelling, function approximation and control. Some applications, referring to attractive themes in system engineering and a MATLAB software tool, are also reported. The appropriate background for this text is a knowledge of neural networks fundamentals. The manuscript is intended as a research report, but a great effort has been performed to make the subject comprehensible to graduate students in computer engineering, control engineering, computer sciences and related disciplines.

Taschenbuch. Zustand: Neu. Neural Networks in Multidimensional Domains | Fundamentals and New Trends in Modelling and Control | Paolo Arena (u. a.) | Taschenbuch | xiv | Englisch | 1998 | Springer | EAN 9781852330064 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Zustand: Neu. Neural Networks for Modelling and Control of Dynamic Systems | A Practitioner's Handbook | M. Norgaard (u. a.) | Taschenbuch | xiv | Englisch | 2000 | Springer London | EAN 9781852332273 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Zustand: 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.

Taschenbuch. Zustand: 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.

Zustand: New. pp. 700.

Taschenbuch. Zustand: Neu. Artificial Neural Networks for Modelling and Control of Non-Linear Systems | Johan A. K. Suykens (u. a.) | Taschenbuch | xii | Englisch | 2010 | Springer US | EAN 9781441951588 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Buch. Zustand: Neu. Neuware -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 NLqTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 252 pp. Englisch.

Zustand: New.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 NLqTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 NLqTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.

Paperback. Zustand: Brand New. 252 pages. 9.00x6.00x0.56 inches. In Stock.

Zustand: New. Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. This volume discusses the properties as well as the applications of artificial neural networks. Num Pages: 247 pages, biography. BIC Classification: UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 15. Weight in Grams: 532. . 1995. Hardback. . . . . Books ship from the US and Ireland.