Neural Networks and Learning Algorithms in MATLAB

Ardashir Mohammadazadeh

ISBN 10: 3031145739 / ISBN 13: 9783031145735

Published by Springer International Publishing, Springer Nature Switzerland, 2023

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783031145735

Bibliographic Details

• Title: Neural Networks and Learning Algorithms in MATLAB
• Publisher: Springer International Publishing, Springer Nature Switzerland
• Publication Date: 2023
• Language: English
• ISBN 10: 3031145739
• ISBN 13: 9783031145735
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 240W x 168H x 8D millimeters 240 millimeters width by 168 millimeters height by 8 millimeters depth
• Weight: 236 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.