Artificial Intelligence Systems Based on Hybrid Neural Networks

Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko

ISBN 10: 3030484521 / ISBN 13: 9783030484521

Published by Springer Nature Switzerland AG, CH, 2020

About this Item

This book is intended for specialists as well as students and graduate students in the field of artificial intelligence, robotics and information technology. It is will also appeal to a wide range of readers interested in expanding the functionality of artificial intelligence systems. One of the pressing problems of modern artificial intelligence systems is the development of integrated hybrid systems based on deep learning. Unfortunately, there is currently no universal methodology for developing topologies of hybrid neural networks (HNN) using deep learning.  The development of such systems calls for the expansion of the use of neural networks (NS) for solving recognition, classification and optimization problems. As such, it is necessary to create a unified methodology for constructing HNN with a selection of models of artificial neurons that make up HNN, gradually increasing the complexity of their structure using hybrid learning algorithms.

Seller Inventory # LU-9783030484521

Bibliographic Details

• Title: Artificial Intelligence Systems Based on Hybrid Neural Networks
• Publisher: Springer Nature Switzerland AG, CH
• Publication Date: 2020
• Language: English
• ISBN 10: 3030484521
• ISBN 13: 9783030484521
• Binding: Hardback
• Edition: 2021 ed.
• Condition: New

About this title

Synopsis

This book is intended for specialists as well as students and graduate students in the field of artificial intelligence, robotics and information technology. It is will also appeal to a wide range of readers interested in expanding the functionality of artificial intelligence systems.

 

One of the pressing problems of modern artificial intelligence systems is the development of integrated hybrid systems based on deep learning. Unfortunately, there is currently no universal methodology for developing topologies of hybrid neural networks (HNN) using deep learning. 

 

The development of such systems calls for the expansion of the use of neural networks (NS) for solving recognition, classification and optimization problems. As such, it is necessary to create a unified methodology for constructing HNN with a selection of models of artificial neurons that make up HNN, gradually increasing the complexity of their structure using hybrid learning algorithms.