Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Applied Artificial Neural Networks
Top Free C Programming Books 🌠 - 100% Free or Open Source!
  • Title: Applied Artificial Neural Networks
  • Author(s) Christian Dawson
  • Publisher: Mdpi AG; 1st edition; eBook (Creative Commons Licensed)
  • License(s): Creative Commons License (CC)
  • Hardcover/Paperback: 244 pages
  • eBook: PDF
  • Language: English
  • ISBN-10: 3038422703
  • ISBN-13: 978-3038422709
  • Share This:  

Book Description

Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves--in terms of training, topologies, types, etc. - a similar amount of work has examined their application to a whole host of real-world problems. Such problems are usually difficult to define and hard to solve using conventional techniques. Examples include computer vision, speech recognition, financial applications, medicine, meteorology, robotics, hydrology, etc.

This Special Issue focuses on the second of these two research themes, that of the application of neural networks to a diverse range of fields and problems. It collates contributions concerning neural network applications in areas such as engineering, hydrology and medicine.

About the Authors
  • João Luís Garcia Rosa is an associate professor at the Department of Computer Science, University of São Paulo (USP) at Sao Carlos, Brazil, where he teaches disciplines such as Neural Networks, Brain-Computer Interfaces, and Artificial Intelligence.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Introduction to Artificial Neural Networks (Jan Larsen, et al.)

    This fundamental book on Artificial Neural Networks (ANN) has its emphasis on clear concepts, ease of understanding and simple examples. It presents a large variety of standard neural networks with architecture, algorithms and applications.

  • Principles of Artificial Neural Networks (Daniel Graupe)

    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.

  • Artificial Neural Networks - Models and Applications

    This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique. It contains chapters on basic concepts of artificial neural networks.

  • Artificial Neural Networks - Architectures and Applications

    This book covers architectures, design, optimization, and analysis of artificial neural networks as well as applications of artificial neural networks in a wide range of areas including biomedical, industrial, physics, and financial applications.

  • Artificial Neural Networks - Methodological Advances and Apps

    The book begins with fundamentals of artificial neural networks, which cover an introduction, design, and optimization. Advanced architectures for biomedical applications, which offer improved performance and desirable properties, follow.

  • Neural Networks - A Systematic Introduction (Raul Rojas)

    In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. It is aimed at readers who seek an overview of the field or who wish to deepen their knowledge.

  • An Introduction to Neural Networks (Kevin Gurney)

    With an easy to understand format using graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics.

  • A Brief Introduction to Neural Networks (David Kriesel)

    Introduces the Java programmer to the world of Neural Networks and Artificial Intelligence using SNIPE. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.

  • Neural Networks with JavaScript Succinctly (James McCaffrey)

    This book leads you through the fundamental concepts of neural networks, including its architecture, its input-output, tanh and softmax activation, back-propagation, error and accuracy, normalization and encoding, and model interpretation.

  • Neural Networks (Rolf Pfeifer, et al)

    Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It is a systematic introduction to neural networks, biological foundation.

  • Memristor and Memristive Neural Networks (Alex James)

    This book covers a range of models, circuits and systems built with memristor devices and networks in applications to neural networks. It is divided into three parts: (1) Devices, (2) Models and (3) Applications.

Book Categories
:
Other Categories
Resources and Links