Principles Of Artificial Neural Networks

Author : Daniel Graupe
Publisher : World Scientific
Page : 500 pages
File Size : 51,9 Mb
Release : 2013-07-31
Category : Computers
ISBN : 9789814522755

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Principles of Artificial Neural Networks by Daniel Graupe Pdf

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. Contents:Introduction and Role of Artificial Neural NetworksFundamentals of Biological Neural NetworksBasic Principles of ANNs and Their Early StructuresThe PerceptronThe MadalineBack PropagationHopfield NetworksCounter PropagationLarge Scale Memory Storage and Retrieval (LAMSTAR) NetworkAdaptive Resonance TheoryThe Cognitron and the NeocognitronStatistical TrainingRecurrent (Time Cycling) Back Propagation Networks Readership: Graduate and advanced senior students in artificial intelligence, pattern recognition & image analysis, neural networks, computational economics and finance, and biomedical engineering. Keywords:Neural Networks;Mathematical Derivations;Source Codes;Medical Applications;Data Mining;Cell-Shape Recognition;Micro-Trading

Author : Daniel Graupe
Publisher : World Scientific
Page : 382 pages
File Size : 46,9 Mb
Release : 2013
Category : COMPUTERS
ISBN : 9789814522748

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Principles of Artificial Neural Networks by Daniel Graupe Pdf

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 : Graupe Daniel
Publisher : World Scientific
Page : 440 pages
File Size : 54,8 Mb
Release : 2019-03-15
Category : Computers
ISBN : 9789811201240

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

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
Page : 256 pages
File Size : 52,9 Mb
Release : 1997-05-01
Category : Mathematics
ISBN : 9810241259

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Principles of Artificial Neural Networks by Daniel Graupe Pdf

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.

Author : Kevin L. Priddy,Paul E. Keller
Publisher : SPIE Press
Page : 184 pages
File Size : 41,7 Mb
Release : 2005
Category : Computers
ISBN : 0819459879

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Artificial Neural Networks by Kevin L. Priddy,Paul E. Keller Pdf

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.

Author : Daniel Graupe
Publisher : World Scientific
Page : 320 pages
File Size : 41,9 Mb
Release : 2007
Category : Computers
ISBN : 9789812770578

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Principles of Artificial Neural Networks by Daniel Graupe Pdf

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 A. Roberts,Sho Yaida,Boris Hanin
Publisher : Cambridge University Press
Page : 473 pages
File Size : 51,9 Mb
Release : 2022-05-26
Category : Computers
ISBN : 9781316519332

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The Principles of Deep Learning Theory by Daniel A. Roberts,Sho Yaida,Boris Hanin Pdf

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Author : P.J. Braspenning,F. Thuijsman,A.J.M.M. Weijters
Publisher : Springer Science & Business Media
Page : 320 pages
File Size : 48,6 Mb
Release : 1995-06-02
Category : Computers
ISBN : 3540594884

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Artificial Neural Networks by P.J. Braspenning,F. Thuijsman,A.J.M.M. Weijters Pdf

This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM.

Author : Russell Reed,Robert J MarksII
Publisher : MIT Press
Page : 359 pages
File Size : 52,7 Mb
Release : 1999-02-17
Category : Computers
ISBN : 9780262181907

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Neural Smithing by Russell Reed,Robert J MarksII Pdf

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.

Author : Ivan Nunes da Silva,Danilo Hernane Spatti,Rogerio Andrade Flauzino,Luisa Helena Bartocci Liboni,Silas Franco dos Reis Alves
Publisher : Springer
Page : 307 pages
File Size : 46,8 Mb
Release : 2016-08-24
Category : Technology & Engineering
ISBN : 9783319431628

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Artificial Neural Networks by Ivan Nunes da Silva,Danilo Hernane Spatti,Rogerio Andrade Flauzino,Luisa Helena Bartocci Liboni,Silas Franco dos Reis Alves Pdf

This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

Author : Giuseppe Ciaburro,Balaji Venkateswaran
Publisher : Packt Publishing Ltd
Page : 270 pages
File Size : 49,8 Mb
Release : 2017-09-27
Category : Computers
ISBN : 9781788399418

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Neural Networks with R by Giuseppe Ciaburro,Balaji Venkateswaran Pdf

Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples.

Author : Lean Yu,Shouyang Wang,Kin Keung Lai
Publisher : Springer Science & Business Media
Page : 316 pages
File Size : 44,9 Mb
Release : 2010-02-26
Category : Business & Economics
ISBN : 9780387717203

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Foreign-Exchange-Rate Forecasting with Artificial Neural Networks by Lean Yu,Shouyang Wang,Kin Keung Lai Pdf

This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.

Author : Sivanandam S., Paulraj M
Publisher : Vikas Publishing House
Page : 236 pages
File Size : 44,6 Mb
Release : 2009-11-01
Category : Computers
ISBN : 9788125914259

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Introduction to Artificial Neural Networks by Sivanandam S., Paulraj M Pdf

This fundamental book on Artificial Neural Networks has its emphasis on clear concepts, ease of understanding and simple examples. Written for undergraduate students, the book presents a large variety of standard neural networks with architecture, algorithms and applications.

Author : Daniel Graupe
Publisher : Unknown
Page : 439 pages
File Size : 47,6 Mb
Release : 2019
Category : COMPUTERS
ISBN : 9811201234

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Principles of Artificial Neural Networks by Daniel Graupe Pdf

Author : Subana Shanmuganathan,Sandhya Samarasinghe
Publisher : Springer
Page : 472 pages
File Size : 46,7 Mb
Release : 2016-02-03
Category : Technology & Engineering
ISBN : 9783319284958

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Artificial Neural Network Modelling by Subana Shanmuganathan,Sandhya Samarasinghe Pdf

This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling.