• Title Advanced Applications for Artificial Neural Networks
• Author(s) Adel El-Shahat
• Publisher: IN-TECH (February 28, 2018)
• License(s): Attribution 3.0 Unported (CC BY 3.0)
• Hardcover 296 pages
• eBook PDF files
• Language: English
• ISBN-10: N/A
• ISBN-13: 978-953-51-3781-8, Print ISBN 978-953-51-3780-1
• Share This:  

Book Description

In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of Artificial Neural Networks (ANN).

It addresses advanced applications and innovative case studies for the next-generation optical networks based on modulation recognition using artificial neural networks, hardware ANN for gait generation of multi-legged robots, production of high-resolution soil property ANN maps, ANN and dynamic factor models to combine forecasts, ANN parameter recognition of engineering constants in Civil Engineering, ANN electricity consumption and generation forecasting, ANN for advanced process control, ANN breast cancer detection, ANN applications in biofuels, ANN modeling for manufacturing process optimization, spectral interference correction using a large-size spectrometer and ANN-based deep learning, solar radiation ANN prediction using NARX model, and ANN data assimilation for an atmospheric general circulation model.

• Dr. Adel El-Shahat is an Assistant Professor in the Department of Electrical and Computer Engineering at Georgia Southern University (GSU), USA. He is Founder and Director of Innovative Power Electronics and Nano-Grids Research Lab (IPENG) at GSU.

• Amazon

• Neural Networks and Deep Learning
• Machine Learning
• Artificial Intelligence

• Advanced Applications for Artificial Neural Networks (Adel El-Shahat)
• PDF Format

• Neural Network Design (Martin T. Hagan)

  This book provides a clear and detailed coverage of fundamental neural network architectures and learning rules. It emphasizes a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems.

• Applied Artificial Neural Networks (Christian Dawson)

  This book focuses on 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.

• 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.

• 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.

• 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.

• Recurrent Neural Networks and Soft Computing (M. ElHefnawi)

  Advanced information regarding the theory, concepts and applications of recurrent neural networks and the field of soft computing has been highlighted in this elaborative book. Additional topics in this vein are the application of AI techniques to electromagnetic interference problems, etc.

• Recurrent Neural Networks for Temporal Data Processing

  By presenting the latest research work the book demonstrates how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction.

• Recurrent Neural Networks (Xiaolin Hu, et al)

  This book investigates the following Recurrent Neural Networks (RNNs) models which solve some practical problems, together with their corresponding analysis on stability and convergence.