Neural Networks and Learning Algorithms in MATLAB

Ardashir Mohammadazadeh

ISBN 10: 3031145704 / ISBN 13: 9783031145704

Published by Springer International Publishing, Springer International Publishing Dez 2022, 2022

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book explains the basic concepts, theory and applications of neural networks in a simple unified approach with clear examples and simulations in the MATLAB programming language. The scripts herein are coded for general purposes to be easily extended to a variety of problems in different areas of application. They are vectorized and optimized to run faster and be applicable to high-dimensional engineering problems. This book will serve as a main reference for graduate and undergraduate courses in neural networks and applications. This book will also serve as a main basis for researchers dealing with complex problems that require neural networks for finding good solutions in areas, such as time series prediction, intelligent control and identification. In addition, the problem of designing neural network by using metaheuristics, such as the genetic algorithms and particle swarm optimization, with one objective and with multiple objectives, is presented. 132 pp. Englisch.

Seller Inventory # 9783031145704

Bibliographic Details

• Title: Neural Networks and Learning Algorithms in MATLAB
• Publisher: Springer International Publishing, Springer International Publishing Dez 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 3031145704
• ISBN 13: 9783031145704
• Binding: Buch
• Condition: Neu
• Dimensions: 246W x 173H x 13D millimeters 246 millimeters width by 173 millimeters height by 13 millimeters depth
• Weight: 411 grams

About this title

Synopsis

This book explains the basic concepts, theory and applications of neural networks in a simple unified approach with clear examples and simulations in the MATLAB programming language. The scripts herein are coded for general purposes to be easily extended to a variety of problems in different areas of application. They are vectorized and optimized to run faster and be applicable to high-dimensional engineering problems. This book will serve as a main reference for graduate and undergraduate courses in neural networks and applications. This book will also serve as a main basis for researchers dealing with complex problems that require neural networks for finding good solutions in areas, such as time series prediction, intelligent control and identification. In addition, the problem of designing neural network by using metaheuristics, such as the genetic algorithms and particle swarm optimization, with one objective and with multiple objectives, is presented.