DocumentCode
1599406
Title
Cellular neural networks: a genetic algorithm for parameters optimization in artificial vision applications
Author
Taraglio, Sergio ; Zanela, Andrea
Author_Institution
ENEA, Rome, Italy
fYear
1996
Firstpage
315
Lastpage
320
Abstract
An optimization method for some of the CNN´s parameters, based on evolutionary strategies, is proposed. The new class of feedback template found is more effective in extracting features from the images that an autonomous vehicle acquires, than in the previous CNN´s literature
Keywords
automatic guided vehicles; cellular neural nets; feature extraction; feedback; genetic algorithms; mobile robots; robot vision; AGV; CNN; artificial vision applications; autonomous vehicle; cellular neural networks; evolutionary strategies; feature extraction; feedback template; genetic algorithm; parameter optimization; Cellular neural networks; Circuits; Feature extraction; Genetic algorithms; Indoor environments; Intelligent networks; Laplace equations; Mobile robots; Navigation; Remotely operated vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and their Applications, 1996. CNNA-96. Proceedings., 1996 Fourth IEEE International Workshop on
Conference_Location
Seville
Print_ISBN
0-7803-3261-X
Type
conf
DOI
10.1109/CNNA.1996.566592
Filename
566592
Link To Document