RTU Research Information System
Latviešu English

Publikācija: 2D-Neo-Fuzzy Neuron and Its Adaptive Learning

Publication Type Publication (anonimusly reviewed) in a journal with an international editorial board indexed in other databases
Funding for basic activity Research project
Defending: ,
Publication language English (en)
Title in original language 2D-Neo-Fuzzy Neuron and Its Adaptive Learning
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Research platform None
Authors Yevgeniy Bodyanskiy
Olena Vynokurova
Valentyna Volkova
Olena Boiko
Keywords 2D network, data mining, hybrid systems, neo-fuzzy neuron
Abstract In the paper, 2D-neo-fuzzy neuron (NFN) is presented. It is a generalization of the traditional NFN for data in matrix form. 2D-NFN is based on the matrix adaptive bilinear model with an additional fuzzification layer. It reduces the number of adjustable synaptic weights in comparison with traditional systems. For its learning, optimized adaptive procedures with filtering and tracking properties are proposed. 2D-NFN can be effectively used for image processing, data reduction, and restoration of non-stationary signals presented as 2D-sequences.
DOI: 10.7250/itms-2018-0003
Hyperlink: https://itms-journals.rtu.lv/article/view/itms-2018-0003 
Reference Bodyanskiy, Y., Vynokurova, O., Volkova, V., Boiko, O. 2D-Neo-Fuzzy Neuron and Its Adaptive Learning. Information Technology and Management Science, 2018, Vol. 21, No. 1, pp.24-28. ISSN 2255-9086. e-ISSN 2255-9094. Available from: doi:10.7250/itms-2018-0003
Full-text Full-text
Publication version
ID 28437