Process Neural Networks


Process Neural Networks
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Process Neural Networks


Process Neural Networks
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Author : Xingui He
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-05

Process Neural Networks written by Xingui He 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 2010-07-05 with Computers categories.


For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.



Process Neural Networks


Process Neural Networks
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Author : Xingui He
language : en
Publisher:
Release Date : 2010

Process Neural Networks written by Xingui He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.


"Process Neural Network: Theory and Applications" proposes the concept and model of a process neural network for the first time, showing how it expands the mapping relationship between the input and output of traditional neural networks and enhances the expression capability for practical problems, with broad applicability to solving problems relating to processes in practice. Some theoretical problems such as continuity, functional approximation capability, and computing capability, are closely examined. The application methods, network construction principles, and optimization algorithms of process neural networks in practical fields, such as nonlinear time-varying system modeling, process signal pattern recognition, dynamic system identification, and process forecast, are discussed in detail. The information processing flow and the mapping relationship between inputs and outputs of process neural networks are richly illustrated. Xingui He is a member of Chinese Academy of Engineering and also a professor at the School of Electronic Engineering and Computer Science, Peking University, China, where Shaohua Xu also serves as a professor.



Efficient Processing Of Deep Neural Networks


Efficient Processing Of Deep Neural Networks
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Author : Vivienne Sze
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-06-24

Efficient Processing Of Deep Neural Networks written by Vivienne Sze and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-24 with Computers categories.


This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of the DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.



Application Of Neural Networks And Other Learning Technologies In Process Engineering


Application Of Neural Networks And Other Learning Technologies In Process Engineering
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Author : I M Mujtaba
language : en
Publisher: World Scientific
Release Date : 2001-04-02

Application Of Neural Networks And Other Learning Technologies In Process Engineering written by I M Mujtaba and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-04-02 with Computers categories.


This book is a follow-up to the IChemE symposium on “Neural Networks and Other Learning Technologies”, held at Imperial College, UK, in May 1999. The interest shown by the participants, especially those from the industry, has been instrumental in producing the book. The papers have been written by contributors of the symposium and experts in this field from around the world. They present all the important aspects of neural network utilisation as well as show the versatility of neural networks in various aspects of process engineering problems — modelling, estimation, control, optimisation and industrial applications. Contents:Modelling and IdentificationHybrid SchemesEstimations and ControlNew Learning TechnologiesExperimental and Industrial Applications Readership: Academic and industrial researchers, chemical engineers and control engineers. Keywords:Modelling;Hybrid Schemes;Technologies;Industrial Applications



Artificial Neural Networks As Models Of Neural Information Processing


Artificial Neural Networks As Models Of Neural Information Processing
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Author : Marcel van Gerven
language : en
Publisher: Frontiers Media SA
Release Date : 2018-02-01

Artificial Neural Networks As Models Of Neural Information Processing written by Marcel van Gerven and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-01 with categories.


Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.



Neural Networks For Intelligent Signal Processing


Neural Networks For Intelligent Signal Processing
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Author : Anthony Zaknich
language : en
Publisher: World Scientific
Release Date : 2003

Neural Networks For Intelligent Signal Processing written by Anthony Zaknich and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Technology & Engineering categories.


This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN.



Neural Networks In Bioprocessing And Chemical Engineering


Neural Networks In Bioprocessing And Chemical Engineering
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Author : D. R. Baughman
language : en
Publisher: Academic Press
Release Date : 2014-06-28

Neural Networks In Bioprocessing And Chemical Engineering written by D. R. Baughman and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Science categories.


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



Neural Networks For Signal Processing


Neural Networks For Signal Processing
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Author : Bart Kosko
language : en
Publisher:
Release Date : 1992

Neural Networks For Signal Processing written by Bart Kosko and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.


Edited by a leading expert in neural networks, this collection of essays explores neural network applications in signal and image processing, function and estimation, robotics and control, associative memories, and electrical and optical neural networks. This reference will be of interest to scientists, engineers, and others working in the neural network field.



Methods And Procedures For The Verification And Validation Of Artificial Neural Networks


Methods And Procedures For The Verification And Validation Of Artificial Neural Networks
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Author : Brian J. Taylor
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-20

Methods And Procedures For The Verification And Validation Of Artificial Neural Networks written by Brian J. Taylor 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 2006-03-20 with Computers categories.


Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. Currently no standards exist to verify and validate neural network-based systems. NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications. Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems. A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book.



Neural Networks For Intelligent Signal Processing


Neural Networks For Intelligent Signal Processing
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Author :
language : en
Publisher:
Release Date :

Neural Networks For Intelligent Signal Processing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.