Neural Network Principles
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Author |
: Robert L. Harvey |
Publisher |
: |
Total Pages |
: 216 |
Release |
: 1994 |
ISBN-10 |
: UOM:39015032527346 |
ISBN-13 |
: |
Rating |
: 4/5 (46 Downloads) |
Synopsis Neural Network Principles by : Robert L. Harvey
Using models of biological systems as springboards to a broad range of applications, this volume presents the basic ideas of neural networks in mathematical form. Comprehensive in scope, Neural Network Principles outlines the structure of the human brain, explains the physics of neurons, derives the standard neuron state equations, and presents the consequences of these mathematical models. Author Robert L. Harvey derives a set of simple networks that can filter, recall, switch, amplify, and recognize input signals that are all patterns of neuron activation. The author also discusses properties of general interconnected neuron groups, including the well-known Hopfield and perception neural networks using a unified approach along with suggestions of new design procedures for both. He then applies the theory to synthesize artificial neural networks for specialized tasks. In addition, Neural Network Principles outlines the design of machine vision systems, explores motor control of the human brain and presents two examples of artificial hand-eye systems, demonstrates how to solve large systems of interconnected neurons, and considers control and modulation in the human brain-mind with insights for a new understanding of many mental illnesses.
Author |
: Daniel A. Roberts |
Publisher |
: Cambridge University Press |
Total Pages |
: 473 |
Release |
: 2022-05-26 |
ISBN-10 |
: 9781316519332 |
ISBN-13 |
: 1316519333 |
Rating |
: 4/5 (32 Downloads) |
Synopsis The Principles of Deep Learning Theory by : Daniel A. Roberts
This volume develops an effective theory approach to understanding deep neural networks of practical relevance.
Author |
: Daniel Graupe |
Publisher |
: World Scientific |
Total Pages |
: 439 |
Release |
: 2019-03-15 |
ISBN-10 |
: 9789811201240 |
ISBN-13 |
: 9811201242 |
Rating |
: 4/5 (40 Downloads) |
Synopsis Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition) by : Daniel Graupe
The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new chapters, updates the earlier edition by highlighting recent developments in Deep-Learning Neural Networks, which are the recent leading approaches to neural networks. Uniquely, the book also includes case studies of applications of neural networks — demonstrating how such case studies are designed, executed and how their results are obtained.The title is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.
Author |
: Daniel Graupe |
Publisher |
: World Scientific |
Total Pages |
: 320 |
Release |
: 2007-04-05 |
ISBN-10 |
: 9789814475563 |
ISBN-13 |
: 9814475564 |
Rating |
: 4/5 (63 Downloads) |
Synopsis Principles Of Artificial Neural Networks (2nd Edition) by : Daniel Graupe
The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.
Author |
: Daniel Graupe |
Publisher |
: World Scientific |
Total Pages |
: 320 |
Release |
: 2007 |
ISBN-10 |
: 9789812706249 |
ISBN-13 |
: 9812706240 |
Rating |
: 4/5 (49 Downloads) |
Synopsis Principles of Artificial Neural Networks by : Daniel Graupe
This book should serves as a self-study course for engineers and computer scientist in the industry. The features include major neural network approaches and architectures with theories and detailed case studies for each of the approaches acompanied by complete computer codes and the corresponding computed results. There is also a chapter on LAMSTAR neural network.
Author |
: Xingui He |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 240 |
Release |
: 2010-07-05 |
ISBN-10 |
: 9783540737629 |
ISBN-13 |
: 3540737626 |
Rating |
: 4/5 (29 Downloads) |
Synopsis Process Neural Networks by : Xingui He
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.
Author |
: Robert L. Harvey |
Publisher |
: |
Total Pages |
: 197 |
Release |
: 1994-01-01 |
ISBN-10 |
: 0130633305 |
ISBN-13 |
: 9780130633309 |
Rating |
: 4/5 (05 Downloads) |
Synopsis Neural Network Principles by : Robert L. Harvey
Using models of biological systems as springboards to a broad range of applications, this volume presents the basic ideas of neural networks in mathematical form. Comprehensive in scope, Neural Network Principles outlines the structure of the human brain, explains the physics of neurons, derives the standard neuron state equations, and presents the consequences of these mathematical models. Author Robert L. Harvey derives a set of simple networks that can filter, recall, switch, amplify, and recognize input signals that are all patterns of neuron activation. The author also discusses properties of general interconnected neuron groups, including the well-known Hopfield and perception neural networks using a unified approach along with suggestions of new design procedures for both. He then applies the theory to synthesize artificial neural networks for specialized tasks. In addition, Neural Network Principles outlines the design of machine vision systems, explores motor control of the human brain and presents two examples of artificial hand-eye systems, demonstrates how to solve large systems of interconnected neurons, and considers control and modulation in the human brain-mind with insights for a new understanding of many mental illnesses.
Author |
: Vahid Asadpour |
Publisher |
: BoD – Books on Demand |
Total Pages |
: 165 |
Release |
: 2018-11-28 |
ISBN-10 |
: 9781789845402 |
ISBN-13 |
: 1789845408 |
Rating |
: 4/5 (02 Downloads) |
Synopsis Digital Systems by : Vahid Asadpour
This book provides an approach toward the applications and principle theory of digital signal processing in modern intelligent systems, biological engineering, telecommunication, and information technology. Assuming the reader already has prior knowledge of signal processing theory, this book will be useful for finding novel methods that fit special needs in digital signal processing (DSP). The combination of signal processing and intelligent systems in hybrid structures rather than serial or parallel processing provide the best mechanism that is a better fit with the comprehensive nature of human. This book is a practical reference that places the emphasis on principles and applications of DSP in digital systems. It covers a broad area of digital systems and applications of machine learning methods including convolutional neural networks, evolutionary algorithms, adaptive filters, spectral estimation, data compression and functional verification. The level of the book is ideal for professional DSP users and useful for graduate students who are looking for solutions to their design problems. The theoretical principles provide the required base for comprehension of the methods and application of modifications for the special needs of practical projects.
Author |
: Daniel Graupe |
Publisher |
: World Scientific |
Total Pages |
: 382 |
Release |
: 2013 |
ISBN-10 |
: 9789814522748 |
ISBN-13 |
: 9814522740 |
Rating |
: 4/5 (48 Downloads) |
Synopsis Principles of Artificial Neural Networks by : Daniel Graupe
Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition OCo all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained. The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining."
Author |
: Daniel Graupe |
Publisher |
: World Scientific |
Total Pages |
: 256 |
Release |
: 1997-05-01 |
ISBN-10 |
: 9810241259 |
ISBN-13 |
: 9789810241254 |
Rating |
: 4/5 (59 Downloads) |
Synopsis Principles of Artificial Neural Networks by : Daniel Graupe
This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.