Artificial Intelligence Systems Based on Hybrid Neural Networks

Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko

ISBN 10: 3030484521 / ISBN 13: 9783030484521

Editore: Springer Nature Switzerland AG, CH, 2020

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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.

Codice articolo LU-9783030484521

Dati bibliografici

• Titolo: Artificial Intelligence Systems Based on Hybrid Neural Networks
• Editore: Springer Nature Switzerland AG, CH
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 3030484521
• ISBN 13: 9783030484521
• Rilegatura: Hardback
• Edizione: 2021 ed.
• Condizione: New

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Riassunto

<div>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.</div><div>&nbsp;</div><div>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.&nbsp;</div><div>&nbsp;</div><div>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.&nbsp;</div>

Dalla quarta di copertina

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.