Artificial Intelligence Systems Based on Hybrid Neural Networks: Theory and Appl

Description: Artificial Intelligence Systems Based on Hybrid Neural Networks by Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko 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. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description 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. 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. Table of 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. Feature 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 Details ISBN3030484556 Author Elena Chumachenko Short Title Artificial Intelligence Systems Based on Hybrid Neural Networks Pages 512 Series Studies in Computational Intelligence Language English Year 2021 ISBN-10 3030484556 ISBN-13 9783030484552 Format Paperback Subtitle Theory and Applications Series Number 904 Publisher Springer Nature Switzerland AG Edition 1st Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Publication Date 2021-09-04 UK Release Date 2021-09-04 Illustrations 215 Illustrations, color; 119 Illustrations, black and white; XV, 512 p. 334 illus., 215 illus. in color. Edition Description 1st ed. 2021 Alternative 9783030484521 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:135427944;

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