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Neural Network Design 2nd Edition

Neural Network Design  2nd Edition Author Martin Hagan
ISBN-10 0971732116
Release 2014-09-01
Pages 800
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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

Neural Network Design Author Martin T. Hagan
ISBN-10 9812403760
Release 2003
Pages
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Neural Network Design has been writing in one form or another for most of life. You can find so many inspiration from Neural Network Design also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Neural Network Design book for free.



Neural Network Design

Neural Network Design Author Martin T. Hagan
ISBN-10 0971732108
Release 2002-01-01
Pages 736
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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.



Fundamentals of Artificial Neural Networks

Fundamentals of Artificial Neural Networks Author Mohamad H. Hassoun
ISBN-10 026208239X
Release 1995
Pages 511
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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



An Introduction to Neural Networks

An Introduction to Neural Networks Author Kevin Gurney
ISBN-10 9781482286991
Release 2014-04-21
Pages 234
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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.



Recurrent Neural Networks

Recurrent Neural Networks Author Larry Medsker
ISBN-10 1420049178
Release 1999-12-20
Pages 416
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With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.



Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks Author Brian D. Ripley
ISBN-10 0521717701
Release 2007
Pages 403
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Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.



Neural Network Programming with Java

Neural Network Programming with Java Author Fabio M. Soares
ISBN-10 9781787122970
Release 2017-03-14
Pages 270
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Create and unleash the power of neural networks by implementing professional Java code About This Book Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition Explore the Java multi-platform feature to run your personal neural networks everywhere This step-by-step guide will help you solve real-world problems and links neural network theory to their application Who This Book Is For This book is for Java developers who want to know how to develop smarter applications using the power of neural networks. Those who deal with a lot of complex data and want to use it efficiently in their day-to-day apps will find this book quite useful. Some basic experience with statistical computations is expected. What You Will Learn Develop an understanding of neural networks and how they can be fitted Explore the learning process of neural networks Build neural network applications with Java using hands-on examples Discover the power of neural network's unsupervised learning process to extract the intrinsic knowledge hidden behind the data Apply the code generated in practical examples, including weather forecasting and pattern recognition Understand how to make the best choice of learning parameters to ensure you have a more effective application Select and split data sets into training, test, and validation, and explore validation strategies In Detail Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out. You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time. All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience. Style and approach This book takes you on a steady learning curve, teaching you the important concepts while being rich in examples. You'll be able to relate to the examples in the book while implementing neural networks in your day-to-day applications.



FPGA Implementations of Neural Networks

FPGA Implementations of Neural Networks Author Amos R. Omondi
ISBN-10 9780387284873
Release 2006-10-04
Pages 360
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During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.



Artificial Neural Networks

Artificial Neural Networks Author Ivan Nunes da Silva
ISBN-10 9783319431628
Release 2016-09-25
Pages 307
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This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.



Introduction to Neural Networks Using Matlab 6 0

Introduction to Neural Networks Using Matlab 6 0 Author S. N. Sivanandam
ISBN-10 0070591121
Release 2006
Pages 656
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Introduction to Neural Networks Using Matlab 6 0 has been writing in one form or another for most of life. You can find so many inspiration from Introduction to Neural Networks Using Matlab 6 0 also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Introduction to Neural Networks Using Matlab 6 0 book for free.



Object oriented Neural Networks in C

Object oriented Neural Networks in C  Author Joey Rogers
ISBN-10 0125931158
Release 1997
Pages 310
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This book/disk package provides the reader with a foundation from which any neural network architecture can be constructed. The author has employed object-oriented design and object-oriented programming concepts to develop a set of foundation neural network classes, and shows how these classes can be used to implement a variety of neural network architecture with a great deal of ease and flexibility.



Neural Networks for Applied Sciences and Engineering

Neural Networks for Applied Sciences and Engineering Author Sandhya Samarasinghe
ISBN-10 1420013068
Release 2016-04-19
Pages 570
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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.



Neural Networks in Chemistry and Drug Design

Neural Networks in Chemistry and Drug Design Author Jure Zupan
ISBN-10 3527297782
Release 1999-10-25
Pages 402
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The second edition of this highly regarded text has been substantially expanded. Part VI "Applications" is updated from 12 to 21 examples with a new focus on applications in the area of drug design. From reviews of the first edition: ?This book offers a sound introduction to artificial neuronal networks, with insights into their architecture, functioning, and applications, which is intended not only for chemists... The excellent quality of the contents and the presentation should ensure that it reaches a wide international readership.?(Angewandte Chemie) 'One of the most useful aspects of the book is a walk-through of the whole process for each application: experimental design, choice and organization of the data, selection of network architecture and parameters, and analysis of the results... The careful approach embodied in this book is an antidote to the hype which has attended neuronal networks in recent years.' (Journal of the American Chemical Society) '... highly recommended ... could become a scientific bestseller ...' (Spectroscopy Europe) 'The attractive and clear presentation of this book make it recommendable to the complete novice.' (The Analyst) 'We strongly recommend it for library purchase and it will be a useful text for lecture courses.' (Chemistry & Industry)



Introduction to Neural Networks with Java

Introduction to Neural Networks with Java Author Jeff Heaton
ISBN-10 9781604390087
Release 2008
Pages 440
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Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward, Hopfield, and Self Organizing Map networks are discussed. Training techniques such as Backpropagation, Genetic Algorithms and Simulated Annealing are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, financial prediction, game strategy, learning mathematical functions and special application to Internet bots. All Java source code can be downloaded online.



The Handbook of Brain Theory and Neural Networks

The Handbook of Brain Theory and Neural Networks Author Michael A. Arbib
ISBN-10 9780262011976
Release 2003
Pages 1290
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A new, dramatically updated edition of the classic resource on the constantly evolving fields of brain theory and neural networks.



Neural Networks

Neural Networks Author Simon Haykin
ISBN-10 0780334949
Release 1999-01
Pages 700
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Neural Networks has been writing in one form or another for most of life. You can find so many inspiration from Neural Networks also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Neural Networks book for free.