Neural Networks For Applied Sciences And Engineering

Author: Sandhya Samarasinghe
Publisher: CRC Press
ISBN: 9781420013061
Size: 17.46 MB
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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 For Applied Sciences And Engineering

Author: Sandhya Samarasinghe
Publisher: Auerbach Publications
ISBN: 9780849333750
Size: 10.51 MB
Format: PDF, Docs
View: 155
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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.

An Introduction To Neural Networks

Author: Kevin Gurney
Publisher: CRC Press
ISBN: 1482286998
Size: 48.25 MB
Format: PDF, ePub, Mobi
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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.

Neural Networks In Bioprocessing And Chemical Engineering

Author: D. R. Baughman
Publisher: Academic Press
ISBN: 1483295656
Size: 74.68 MB
Format: PDF
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Neural networks have received a great deal of attention among scientists and engineers. In chemical engineering, neural computing has moved from pioneering projects toward mainstream industrial applications. This book introduces the fundamental principles of neural computing, and is the first to focus on its practical applications in bioprocessing and chemical engineering. Examples, problems, and 10 detailed case studies demonstrate how to develop, train, and apply neural networks. A disk containing input data files for all illustrative examples, case studies, and practice problems provides the opportunity for hands-on experience. An important goal of the book is to help the student or practitioner learn and implement neural networks quickly and inexpensively using commercially available, PC-based software tools. Detailed network specifications and training procedures are included for all neural network examples discussed in the book. Each chapter contains an introduction, chapter summary, references to further reading, practice problems, and a section on nomenclature Includes a PC-compatible disk containing input data files for examples, case studies, and practice problems Presents 10 detailed case studies Contains an extensive glossary, explaining terminology used in neural network applications in science and engineering Provides examples, problems, and ten detailed case studies of neural computing applications, including: Process fault-diagnosis of a chemical reactor Leonard Kramer fault-classification problem Process fault-diagnosis for an unsteady-state continuous stirred-tank reactor system Classification of protein secondary-structure categories Quantitative prediction and regression analysis of complex chemical kinetics Software-based sensors for quantitative predictions of product compositions from flourescent spectra in bioprocessing Quality control and optimization of an autoclave curing process for manufacturing composite materials Predictive modeling of an experimental batch fermentation process Supervisory control of the Tennessee Eastman plantwide control problem Predictive modeling and optimal design of extractive bioseparation in aqueous two-phase systems

Exploring Neural Networks With C

Author: Ryszard Tadeusiewicz
Publisher: CRC Press
ISBN: 1498760376
Size: 30.90 MB
Format: PDF, Kindle
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The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations—making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the important properties of neural networks—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand. Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks. Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en

Neural Network Design 2nd Edition

Author: Martin Hagan
Publisher:
ISBN: 9780971732117
Size: 55.86 MB
Format: PDF, Kindle
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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 Networks For Pattern Recognition

Author: Christopher M. Bishop
Publisher: Oxford University Press
ISBN: 0198538642
Size: 72.83 MB
Format: PDF, Mobi
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`Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition' New Scientist

Pattern Recognition In Industry

Author: Phiroz Bhagat
Publisher: Elsevier
ISBN: 9780080456027
Size: 24.31 MB
Format: PDF, Kindle
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"Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?" If so, this book will help, as it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business models to gain competitive industrial advantage. *Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry. Phiroz Bhagat is often referred to as the pioneer of neural net and pattern recognition technology, and is uniquely qualified to write this book. He brings more than two decades of experience in the "real-world" application of cutting-edge technology for competitive advantage in industry. Two wave fronts are upon us today: we are being bombarded by an enormous amount of data, and we are confronted by continually increasing technical and business advances. Ideally, the endless stream of data should be one of our major assets. However, this potential asset often tends to overwhelm rather than enrich. Competitive advantage depends on our ability to extract and utilize nuggets of valuable knowledge and insight from this data deluge. The challenges that need to be overcome include the under-utilization of available data due to competing priorities, and the separate and somewhat disparate existing data systems that have difficulty interacting with each other. Conventional approaches to formulating models are becoming progressively more expensive in time and effort. To impart a competitive edge, engineering science in the 21st century needs to augment traditional modelling processes by auto-classifying and self-organizing data; developing models directly from operating experience, and then optimizing the results to provide effective strategies and operating decisions. This approach has wide applicability; in areas ranging from manufacturing processes, product performance and scientific research, to financial and business fields. This monograph explores pattern recognition technology, and its concomitant role in extracting useful knowledge to build technical and business models directly from data, and in optimizing the results derived from these models within the context of delivering competitive industrial advantage. It is not intended to serve as a comprehensive reference source on the subject. Rather, it is based on first-hand experience in the practice of this technology: its development and deployment for profitable application in industry. The technical topics covered in the monograph will focus on the triad of technological areas that constitute the contemporary workhorses of successful industrial application of pattern recognition. These are: systems for self-organising data; data-driven modelling; and genetic algorithms as robust optimizers. "Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?" If so, this book will help, as it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business models to gain competitive industrial advantage. Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry. Phiroz Bhagat is often referred to as the pioneer of neural net and pattern recognition technology, and is uniquely qualified to write this book. He brings more than two decades of experience in the "real-world" application of cutting-edge technology for competitive advantage in industry.

Fundamentals Of Artificial Neural Networks

Author: Mohamad H. Hassoun
Publisher: MIT Press
ISBN: 9780262082396
Size: 56.11 MB
Format: PDF
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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

Pattern Recognition For Medical Imaging

Author: Anke Meyer-Bäse
Publisher: Academic Press
ISBN: 0124932908
Size: 65.11 MB
Format: PDF, ePub
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Essential tool for students and professionals that compiles and explains proven and cutting-edge methods in pattern recognition for medical imaging.