Designs And Applied Principles Of Artificial Neural Networks
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Designs and Applied Principles of Artificial Neural Networks
This book presents a broad overview on the current various design modules and applications of artificial neural systems. Artificial neural networks are often dubbed as one of the most acknowledged technologies in the past twenty years. It has been broadly applied in a wide diversity of applications. The aim of this book is to present current developments of architectures, methodologies, and usage of artificial neural networks. The topics in this book majorly deal with two important aspects: the architecture aspect involving dealing with architectures, design, optimization, and the analysis of artificial neural networks; and the application aspect dealing with applications of artificial neural networks in a broad spectrum of areas comprising of biomedical, industrial, physics, and financial applications. This book will be an important source of reference for graduate students and engineers in various organizations.
Principles Of Artificial Neural Networks: Basic Designs To Deep Learning (4th Edition)
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
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."