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Principles of Artificial Neural Networks

Author: Daniel Graupe

Publisher: World Scientific

Published: 2007

Total Pages: 320

ISBN-13: 9812706240

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.

Principles of Artificial Neural Networks

Author: Daniel Graupe

Publisher: World Scientific

Published: 1997-05-01

Total Pages: 256

ISBN-13: 9789810241254

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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.

Principles Of Artificial Neural Networks (3rd Edition)

Author: Daniel Graupe

Publisher: World Scientific

Published: 2013-07-31

Total Pages: 382

ISBN-13: 9814522759

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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 — 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.

Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)

Author: Graupe Daniel

Publisher: World Scientific

Published: 2019-03-15

Total Pages: 440

ISBN-13: 9811201242

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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.

Principles of Artificial Neural Networks

Author: Daniel Graupe

Publisher:

Published: 2019

Total Pages: 439

ISBN-13: 9789811201233

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Artificial Neural Networks

Author: Kevin L. Priddy

Publisher: SPIE Press

Published: 2005

Total Pages: 184

ISBN-13: 9780819459879

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This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.

Fundamentals of Artificial Neural Networks

Author: Mohamad H. Hassoun

Publisher: MIT Press

Published: 1995

Total Pages: 546

ISBN-13: 9780262082396

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A systematic account of artificial neural network paradigms that identifies fundamental concepts and major methodologies. Important results are integrated into the text in order to explain a wide range of existing empirical observations and commonly used heuristics.

Artificial Neural Network Training and Software Implementation Techniques

Author: Ali Kattan

Publisher: Nova Novinka

Published: 2011

Total Pages: 58

ISBN-13: 9781611229905

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Artificial neural networks (ANN) are widely used in diverse fields of science and industry. Though there have been numerous techniques used for their implementations, the choice of a specific implementation is subjected to different factors including cost, accuracy, processing speed and overall performance. Featured with synaptic plasticity, the process of training is concerned with adjusting the individual weights between each of the individual ANN neurons until we can achieve close to the desired output. This book introduces the common trajectory-driven and evolutionary-based ANN training algorithms.

Artificial Neural Networks for Engineers and Scientists

Author: S. Chakraverty

Publisher: CRC Press

Published: 2017-07-20

Total Pages: 156

ISBN-13: 1351651315

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Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence methods, such as Artificial Neural Networks (ANN), are being used to solve differential equations, and these methods are presented in Artificial Neural Networks for Engineers and Scientists: Solving Ordinary Differential Equations. This book shows how computation of differential equation becomes faster once the ANN model is properly developed and applied.