Overview
- Presents a unified methodology for constructing hybrid neural networks
- Provides original research findings on artificial intelligence systems based on hybrid neural networks
- Includes theory and applications
Part of the book series: Studies in Computational Intelligence (SCI, volume 904)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (8 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Artificial Intelligence Systems Based on Hybrid Neural Networks
Book Subtitle: Theory and Applications
Authors: Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-48453-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-48452-1Published: 04 September 2020
Softcover ISBN: 978-3-030-48455-2Published: 04 September 2021
eBook ISBN: 978-3-030-48453-8Published: 03 September 2020
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XV, 512
Number of Illustrations: 119 b/w illustrations, 215 illustrations in colour