Artificial Intelligence Systems Based on Hybrid Neural Networks: Theory and Applications (Studies in Computational Intelligence, 904)
Language: English
Published by Springer, 2020
Series: Book 414 of 538 - Studies in Computational Intelligence
- Hardcover
- New
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- Title
- Artificial Intelligence Systems Based on Hybrid Neural Networks: Theory and Applications (Studies in Computational Intelligence, 904)
- Author
- Zgurovsky, Michael; Sineglazov, Victor; Chumachenko, Elena
- Publisher
- Springer
- Publication year
- 2020
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 3030484521
- ISBN 13
- 9783030484521
- Series
- Book 414 of 538: Studies in Computational Intelligence
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Books Puddle
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