Artificial Intelligence Systems Based on Hybrid Neural Networks : Theory and Applications / by Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko.

By:

Zgurovsky, Michael

Contributor(s):

Material type: Text

TextSeries: Studies in Computational Intelligence ; 904Publication details: Switzerland: Springer, C2021.Edition: 1st ed. 2021Description: 1 online resource (XV, 512 p. 334 illus., 215 illus. in color.) online resource. 23 cmISBN:

9783030484538
9783030484521
9783030484545
9783030484552

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:

006.3 23 ZGU-A 2021 789477

Online resources:

Full text available from Springer Intelligent Technologies and Robotics eBooks 2021 English/International

Contents:

Classification and Analysis Topologies Known Artificial Neurons and Neural Networks -- Classification and Analysis of Multicriteria Optimization Methods -- Formation of Hybrid Artificial Neural Networks Topologies -- Development of Hybrid Neural Networks -- Intelligence Methods of Forecasting -- Intelligent System of Thyroid Pathology Diagnostics -- Intelligent Automated Road Management Systems -- Fire Surveillance Information Systems.

In: Springer Nature eBookSummary: 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. .

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Holdings ( 1 )
Title notes ( 3 )

Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode	Item holds
Books	Faculty of CS & IT Library CS & IT Shelf No. 44 New Arrival	Book	006.3 ZGU-A 2021 789477 (Browse shelf(Opens below))	C 1	Available		789477

Total holds: 0

Index.

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. .