A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Hybrid Neuro-Fuzzy Model with Immune Training for Recognition of Objects in an Image
2020
International Conference on Information Control Systems & Technologies
Modern systems for image processing and analysis are characterized by the active use of artificial neural networks, for training of which, as a rule, gradient methods are used, but their main limitation of the implementation is high computational cost. The use of the principles of hybridization of neural networks, fuzzy logic and evolutionary algorithms allows you to create new types of models that have a higher recognition quality while reducing the computational cost of training. A hybrid
dblp:conf/icst2/KorablyovAFC20
fatcat:acwkcxtlvfaqfgestvbqrhznu4