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Neural Network Design 2nd Edition


Author : Martin Hagan
language : en
Publisher:
Release Date : 2014-09-01

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This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.

Neural Network Design


Author : Martin T. Hagan
language : en
Publisher:
Release Date : 2003

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Download Neural Network Design written by Martin T. Hagan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Neural networks (Computer science) categories.




Neural Network Design


Author : Martin T. Hagan
language : en
Publisher: Martin Hagan
Release Date : 2002-01-01

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Download Neural Network Design written by Martin T. Hagan and has been published by Martin Hagan this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-01-01 with Neural networks (Computer science) categories.


This book provides a clear and detailed survey of basic neural network architectures and learning rules. In it, the authors emphasize mathematical analysis of networks, methods for training networks, and application of networks to practical engineering problems in pattern recognition, signal processing, and control systems.

An Introduction To Neural Networks


Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 2003-12-16

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Download An Introduction To Neural Networks written by Kevin Gurney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-12-16 with Computers categories.


Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Neural Networks For Applied Sciences And Engineering


Author : Sandhya Samarasinghe
language : en
Publisher: CRC Press
Release Date : 2016-04-19

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Download Neural Networks For Applied Sciences And Engineering written by Sandhya Samarasinghe and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Computers categories.


In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features § Explains neural networks in a multi-disciplinary context § Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting § Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA. Her neural networks research focuses on theoretical understanding and advancements as well as practical implementations.

Fundamentals Of Artificial Neural Networks


Author : Mohamad H. Hassoun
language : en
Publisher: MIT Press
Release Date : 1995

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Download Fundamentals Of Artificial Neural Networks written by Mohamad H. Hassoun and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.


Fundamentals of Building Energy Dynamics assesses how and why buildings use energy, and how energy use and peak demand can be reduced. It provides a basis for integrating energy efficiency and solar approaches in ways that will allow building owners and designers to balance the need to minimize initial costs, operating costs, and life-cycle costs with need to maintain reliable building operations and enhance environmental quality both inside and outside the building. Chapters trace the development of building energy systems and analyze the demand side of solar applications as a means for determining what portion of a building's energy requirements can potentially be met by solar energy.Following the introduction, the book provides an overview of energy use patterns in the aggregate U.S. building population. Chapter 3 surveys work on the energy flows in an individual building and shows how these flows interact to influence overall energy use. Chapter 4 presents the analytical methods, techniques, and tools developed to calculate and analyze energy use in buildings, while chapter 5 provides an extensive survey of the energy conservation and management strategies developed in the post-energy crisis period.The approach taken is a commonsensical one, starting with the proposition that the purpose of buildings is to house human activities, and that conservation measures that negatively affect such activities are based on false economies. The goal is to determine rational strategies for the design of new buildings, and the retrofit of existing buildings to bring them up to modern standards of energy use. The energy flows examined are both large scale (heating systems) and small scale (choices among appliances).Solar Heat Technologies: Fundamentals and Applications, Volume 4

Neural Network Models


Author : Philippe de Wilde
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29

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Download Neural Network Models written by Philippe de Wilde and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Technology & Engineering categories.


Providing an in-depth treatment of neural network models, this volume explains and proves the main results in a clear and accessible way. It presents the essential principles of nonlinear dynamics as derived from neurobiology, and investigates the stability, convergence behaviour and capacity of networks.