Artificial Neural Networks for Modelling and Control of Non-Linear Systems, Johan A.K. Suykens; Joos P.L. Vandewalle; B.L. de

Автор: Sul
Название: Control of Electric Machine Drive Systems
ISBN: 0470590793 ISBN-13(EAN): 9780470590799
Издательство: Wiley
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Цена: 139340.00 T
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Описание: Based on the author`s industry experience and collaborative works with other industries, Control of Electric Machine Drive System is packed with implemented, tested, and verified ideas that relate to everyday problems in the field.

Автор: Norgaard M., Ravn O., Poulsen N.K., Hansen L.K.
Название: Neural Networks for Modelling and Control of Dynamic Systems / A Practitioner`s Handbook
ISBN: 1852332271 ISBN-13(EAN): 9781852332273
Издательство: Springer
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Цена: 74530.00 T
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Описание: 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.

Название: A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
ISBN: 1849968675 ISBN-13(EAN): 9781849968676
Издательство: Springer
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Цена: 156720.00 T
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Описание: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.

Автор: Sied Mehdi Fakhraie; Kenneth C. Smith
Название: VLSI — Compatible Implementations for Artificial Neural Networks
ISBN: 0792398254 ISBN-13(EAN): 9780792398257
Издательство: Springer
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Цена: 158380.00 T
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Описание: Introduces an approach to biologically-inspired and VLSI-compatible definition, simulation, and implementation of artificial neural networks. This book develops a set of guidelines for general hardware implementation of artificial neural networks. It provides a geometrical interpretation of the behavior of different variants of these networks.

Автор: Krzysztof J. Latawiec; Marian ?ukaniszyn; Rafa? St
Название: Advances in Modelling and Control of Non-integer-Order Systems
ISBN: 3319098993 ISBN-13(EAN): 9783319098999
Издательство: Springer
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Цена: 191560.00 T
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Описание: This volume presents selected aspects of non-integer, or fractional order systems, whose analysis, synthesis and applications have increasingly become a real challenge for various research communities, ranging from science to engineering.

Автор: Krzysztof J. Latawiec; Marian ?ukaniszyn; Rafa? St
Название: Advances in Modelling and Control of Non-integer-Order Systems
ISBN: 3319360477 ISBN-13(EAN): 9783319360478
Издательство: Springer
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Цена: 156720.00 T
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Описание: This volume presents selected aspects of non-integer, or fractional order systems, whose analysis, synthesis and applications have increasingly become a real challenge for various research communities, ranging from science to engineering.

Название: Vlsi for neural networks and artificial intelligence
ISBN: 1489913335 ISBN-13(EAN): 9781489913333
Издательство: Springer
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Цена: 153720.00 T
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Описание: Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence.

Автор: Petia Koprinkova-Hristova; Valeri Mladenov; Nikola
Название: Artificial Neural Networks
ISBN: 3319099027 ISBN-13(EAN): 9783319099026
Издательство: Springer
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Цена: 232910.00 T
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The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.

Автор: Petia Koprinkova-Hristova; Valeri Mladenov; Nikola
Название: Artificial Neural Networks
ISBN: 3319349503 ISBN-13(EAN): 9783319349503
Издательство: Springer
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Цена: 174130.00 T
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Описание:

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new algorithms for prototype selection, and group structure discovering. Moreover, the book discusses one-class support vector machines for pattern recognition, handwritten digit recognition, time series forecasting and classification, and anomaly identification in data analytics and automated data analysis. By presenting the state-of-the-art and discussing the current challenges in the fields of artificial neural networks, bioinformatics and neuroinformatics, the book is intended to promote the implementation of new methods and improvement of existing ones, and to support advanced students, researchers and professionals in their daily efforts to identify, understand and solve a number of open questions in these fields.

Автор: Mohamed I. Elmasry
Название: VLSI Artificial Neural Networks Engineering
ISBN: 0792394933 ISBN-13(EAN): 9780792394938
Издательство: Springer
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Цена: 174150.00 T
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Описание: Offers an engineering approach to the design of VLSI Artificial Neural Networks (ANNs). This title presents the design of analog, digital and mixed analog/digital VLSI ANNs. It provides a design methodology and a CAD environment to highlight the tradeoff design factors. It includes system applications of ANNs to automatic speech recognition.

Автор: Nicolaos Karayiannis; Anastasios N. Venetsanopoulo
Название: Artificial Neural Networks
ISBN: 1441951326 ISBN-13(EAN): 9781441951328
Издательство: Springer
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Цена: 181630.00 T
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Описание: In order to understand the functioning of the brain, neurobiologists have taken a bottom-up approach of studying the stimulus-response characteristics of single neurons and networks of neurons, while psy- chologists have taken a top-down approach of studying brain func- tions from the cognitive and behavioral level.

Автор: R.S. Govindaraju; A.R. Rao
Название: Artificial Neural Networks in Hydrology
ISBN: 9048154219 ISBN-13(EAN): 9789048154210
Издательство: Springer
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Цена: 174130.00 T
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Описание: , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron.