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Artificial intelligence systems base...

Chumachenko, Elena.

Artificial intelligence systems based on hybrid neural networkstheory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence systems based on hybrid neural networksby Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko.
Reminder of title:	theory and applications /
Author:	Zgurovsky, Michael.
other author:	Sineglazov, Victor.
Published:	Cham :Springer International Publishing :2021.
Description:	xv, 512 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-030-48453-8
ISBN:	9783030484538$q(electronic bk.)

Artificial intelligence systems based on hybrid neural networkstheory and applications /
Zgurovsky, Michael.

Artificial intelligence systems based on hybrid neural networkstheory and applications /[electronic resource] :by Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko. - Cham :Springer International Publishing :2021. - xv, 512 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.9041860-949X ;. - Studies in computational intelligence ;v. 216..

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.

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.

ISBN: 9783030484538$q(electronic bk.)

Standard No.: 10.1007/978-3-030-48453-8doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335 / .Z48 2021

Dewey Class. No.: 006.3

Artificial intelligence systems based on hybrid neural networkstheory and applications /
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520 $a 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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