Neural Networks, Raul Rojas

Автор: Lin Xiao, Lei Jia
Название: Zeroing Neural Networks: Finite-time Convergence Design, Analysis and Applications
ISBN: 1119985994 ISBN-13(EAN): 9781119985990
Издательство: Wiley
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Цена: 111930.00 T
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Описание: Zeroing Neural Networks Describes the theoretical and practical aspects of finite-time ZNN methods for solving an array of computational problems Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven time-varying matrix problems in engineering, control theory, and on-chip applications for robots. Building on the original ZNN model, finite-time zeroing neural networks (FTZNN) enable efficient, accurate, and predictive real-time computations. Setting up discretized FTZNN algorithms for different time-varying matrix problems requires distinct steps.

Zeroing Neural Networks provides in-depth information on the finite-time convergence of ZNN models in solving computational problems. Divided into eight parts, this comprehensive resource covers modeling methods, theoretical analysis, computer simulations, nonlinear activation functions, and more. Each part focuses on a specific type of time-varying computational problem, such as the application of FTZNN to the Lyapunov equation, linear matrix equation, and matrix inversion.

Throughout the book, tables explain the performance of different models, while numerous illustrative examples clarify the advantages of each FTZNN method. In addition, the book: Describes how to design, analyze, and apply FTZNN models for solving computational problems Presents multiple FTZNN models for solving time-varying computational problems Details the noise-tolerance of FTZNN models to maximize the adaptability of FTZNN models to complex environments Includes an introduction, problem description, design scheme, theoretical analysis, illustrative verification, application, and summary in every chapter Zeroing Neural Networks: Finite-time Convergence Design, Analysis and Applications is an essential resource for scientists, researchers, academic lecturers, and postgraduates in the field, as well as a valuable reference for engineers and other practitioners working in neurocomputing and intelligent control.

Автор: Amit Kumar Tyagi, Ajith Abraham
Название: Recurrent neural networks :
ISBN: 1032081643 ISBN-13(EAN): 9781032081649
Издательство: Taylor&Francis
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Цена: 168430.00 T
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Описание: This book comprehensively covers concepts of recurrent neural networks and discusses practical issues such as predictability and nonlinearity detecting. It will an ideal text for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering.

Автор: J.G. Taylor; E.R. Caianiello; R.M.J. Cotterill; J.
Название: Neural Network Dynamics
ISBN: 3540197710 ISBN-13(EAN): 9783540197713
Издательство: Springer
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Цена: 81050.00 T
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Описание: The proceedings of a workshop which encompassed a range of topics in which neural networks played a fundamental role. Attempting to bridge the gap between neural computation and computational neuroscience, the papers describe the foundations of neural network dynamics and their applications.

Автор: Alessandra Lintas; Stefano Rovetta; Paul F.M.J. Ve
Название: Artificial Neural Networks and Machine Learning – ICANN 2017
ISBN: 3319686119 ISBN-13(EAN): 9783319686110
Издательство: Springer
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Цена: 102480.00 T
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Описание: The two volume set, LNCS 10613 and 10614, constitutes the proceedings of then 26th International Conference on Artificial Neural Networks, ICANN 2017, held in Alghero, Italy, in September 2017. The 128 full papers included in this volume were carefully reviewed and selected from 270 submissions.

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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Цена: 51750.00 T
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Описание: This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. Valuable advice is included on both theory and applications, while case studies based on real data sets help readers develop their understanding. All data sets are available from www.stats.ox.ac.uk/~ripley/PRbook/

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.

Автор: Barbara Hammer
Название: Learning with Recurrent Neural Networks
ISBN: 185233343X ISBN-13(EAN): 9781852333430
Издательство: Springer
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Цена: 104480.00 T
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Описание: Folding networks, a generalization of recurrent neural networks to tree structured inputs, are investigated as a mechanism to learn regularities on classical symbolic data. Also, the architecture, the training mechanism, and several applications in different areas are explained in this work.

Автор: H. Malmgren; M. Borga; L. Niklasson
Название: Artificial Neural Networks in Medicine and Biology
ISBN: 1852332891 ISBN-13(EAN): 9781852332891
Издательство: Springer
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Цена: 153720.00 T
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Описание: This volume comprises a selection of papers focusing specifically on the topics of ANNs in medicine and biology. It covers three main areas: the medical applications of ANNs, such as in diagnosis and outcome prediction, medical image analysis, and medical signal processing.

Автор: Maria Marinaro; Pietro G. Morasso
Название: ICANN `94
ISBN: 3540198873 ISBN-13(EAN): 9783540198871
Издательство: Springer
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Цена: 81050.00 T
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Описание: From its early beginnings in the fifties and sixties the field of neural networks has been steadily growing. Researchers from such diverse areas as psychology, mathematics, physics, neuroscience and engineering now work together in the neural networking field.

Автор: Stan Gielen; Bert Kappen
Название: ICANN `93
ISBN: 3540198393 ISBN-13(EAN): 9783540198390
Издательство: Springer
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Цена: 81050.00 T
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Описание: A survey of current research in the field of neural networks, which describes computerized models of higher order human brain functions, the relationship between neural network architecture and function, and the need to extract computational principles from neurobiological findings.

Автор: Zenon Waszczysznk
Название: Neural Networks in the Analysis and Design of Structures
ISBN: 3211833226 ISBN-13(EAN): 9783211833223
Издательство: Springer
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Цена: 81050.00 T
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Описание: Neural Networks are a new, interdisciplinary tool for information processing. Neurocomputing being successfully introduced to structural problems which are difficult or even impossible to be analysed by standard computers (hard computing).

Автор: Bert Kappen; Stan Gielen
Название: Neural Networks: Artificial Intelligence and Industrial Applications
ISBN: 3540199926 ISBN-13(EAN): 9783540199922
Издательство: Springer
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Цена: 81050.00 T
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Описание: This volume contains papers presented at the Third Annual SNN Symposium on Neural Networks held in Nijmegen, 1995. It summarizes developments in neurobiology, the cognitive sciences, robotics, and vision and data modelling. Working neural network solutions to industrial problems are also presented.