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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v45.i3.30
pages 23-33

Self-Learning Cascade Spiking Neural Network for Fuzzy Clustering Based on Group Method of Data Handling

Yevgeniy .V. Bodyanskiy
Kharkov National University of Radioelectronics
Elena A. Vynokurova
Kharkov National University of Radioelectronics
Artem I. Dolotov
Kharkov National University of Radioelectronics

ABSTRAKT

The fuzzy clustering problem in the presence of overlapping classes is considered. To solve the problem, it is introduced the architecture and learning algorithm of a fuzzy spiking neural network, generalizing neural networks of the third generation, which at present are developing intensively and have a number of advantages over traditional computational intelligence systems. The spiking neuron is described as a nonlinear dynamic system, which simplifies the hardware implementation. For problems with high dimension of input vectors-images it is proposed to use the hybrid architecture, which is based on a combination of cascade and GMDH-neural networks with self-learning cascade spiking neural networks, used as nodes, and ensures the increased speed of information processing.


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