Open AccessON-LINE NEO-PHASE AUTOENKODER FOR SYSTEMS WITH DEEP LEARNING ON THE BASE OF THE KOLMOGOROV’S NEURO-PHASE NETWORK
Author(s) -
Є. В. Бодянський,
О.А. Винокурова,
Dmytro Peleshko,
Ю. Рашкевич
Publication year - 2017
Publication title -
rozvitok transportu
Language(s) - English
Resource type - Journals
ISSN - 2616-7360
DOI - 10.33082/td.2017.1-1.06
Subject(s) - autoencoder , computer science , artificial neural network , artificial intelligence , deep learning , process (computing) , task (project management) , line (geometry) , phase (matter) , mode (computer interface) , encoder , fuzzy logic , extension (predicate logic) , pattern recognition (psychology) , mathematics , engineering , chemistry , systems engineering , organic chemistry , operating system , geometry , programming language
One of the important problem, which is connected with big high dimensional data processing, is the task of their compression without significant loss of information that is contained in this data. The systems, which solve this problem and are called autoencoders, are the inherent part of deep neural networks. The main disadvantage of well-known autoencoders is low speed of learning process, which is implemented in the batch mode. In the paper the two-layered autoencoder is proposed. This system is the modification of Kolmogorov’s neuro-fuzzy system. Thus, in the paper the hybrid neo-fuzzy syste- mencoder is proposed that has essentially advantages comparatively with conventional neurocompressors-encoders.