Artificial intelligence systems based on hybrid neural networks : theory and applications
- Michael Zgurovsky, Victor Sineglazov, Elena Chumachenko.
- Cham : Springer, 
- Physical description
- 1 online resource
- Studies in computational intelligence ; v. 904.
- Includes bibliographical references and index.
- 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.
- (source: Nielsen Book Data)
- Publisher's summary
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. .
(source: Nielsen Book Data)
- Publication date
- Studies in computational intelligence, 1860-949X ; v. 904
- 9783030484538 (electronic bk.)
- 303048453X (electronic bk.)