Neural Network Design
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Neural Network Design
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Author : Martin T. Hagan
language : en
Publisher:
Release Date : 2003
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
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Author : Martin T. Hagan
language : en
Publisher:
Release Date : 1996
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 1996 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 appication of networks to practical engineering problems in pattern recognition, signal processing, and control systems.
Neural Network Design
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Author : Martin T. Hagan
language : en
Publisher:
Release Date : 1996-01-01
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 1996-01-01 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 appication of networks to practical engineering problems in pattern recognition, signal processing, and control systems.
Neural Network Design And The Complexity Of Learning
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Author : J. Stephen Judd
language : en
Publisher: MIT Press
Release Date : 1990
Neural Network Design And The Complexity Of Learning written by J. Stephen Judd and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.Judd looks beyond the scope of any one particular learning rule, at a level above the details of neurons. There he finds new issues that arise when great numbers of neurons are employed and he offers fresh insights into design principles that could guide the construction of artificial and biological neural networks.The first part of the book describes the motivations and goals of the study and relates them to current scientific theory. It provides an overview of the major ideas, formulates the general learning problem with an eye to the computational complexity of the task, reviews current theory on learning, relates the book's model of learning to other models outside the connectionist paradigm, and sets out to examine scale-up issues in connectionist learning.Later chapters prove the intractability of the general case of memorizing in networks, elaborate on implications of this intractability and point out several corollaries applying to various special subcases. Judd refines the distinctive characteristics of the difficulties with families of shallow networks, addresses concerns about the ability of neural networks to generalize, and summarizes the results, implications, and possible extensions of the work. Neural Network Design and the Complexity of Learning is included in the Network Modeling and Connectionism series edited by Jeffrey Elman.
Neural Networks And Systolic Array Design
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Author : Sankar K. Pal
language : en
Publisher: World Scientific
Release Date : 2002
Neural Networks And Systolic Array Design written by Sankar K. Pal and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.
Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.
Recurrent Neural Networks
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Author : Larry Medsker
language : en
Publisher: CRC Press
Release Date : 1999-12-20
Recurrent Neural Networks written by Larry Medsker and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-12-20 with Computers categories.
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.
Deep Learning Neural Networks Design And Case Studies
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Author : Daniel Graupe
language : en
Publisher: World Scientific Publishing Company
Release Date : 2016-07-07
Deep Learning Neural Networks Design And Case Studies written by Daniel Graupe and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-07 with Computers categories.
Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving prediction, decision, diagnosis, detection and decision problems based on a well-defined computational architecture. It has been successfully applied to a broad field of applications ranging from computer security, speech recognition, image and video recognition to industrial fault detection, medical diagnostics and finance.This comprehensive textbook is the first in the new emerging field. Numerous case studies are succinctly demonstrated in the text. It is intended for use as a one-semester graduate-level university text and as a textbook for research and development establishments in industry, medicine and financial research.
Vlsi Design Of Neural Networks
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Author : Ulrich Ramacher
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Vlsi Design Of Neural Networks written by Ulrich Ramacher 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 2012-12-06 with Technology & Engineering categories.
The early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.
Neural Networks In Design And Manufacturing
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Author : Jun Wang
language : en
Publisher: World Scientific Publishing Company Incorporated
Release Date : 1993-01-01
Neural Networks In Design And Manufacturing written by Jun Wang and has been published by World Scientific Publishing Company Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-01-01 with Computers categories.
Mathematical Methods For Neural Network Analysis And Design
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Author : Richard M. Golden
language : en
Publisher: MIT Press
Release Date : 1996
Mathematical Methods For Neural Network Analysis And Design written by Richard M. Golden and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
For convenience, many of the proofs of the key theorems have been rewritten so that the entire book uses a relatively uniform notion.