Open AccessModified generalized neo-fuzzy system with combined online fast learning in medical diagnostic task for situations of information deficit
Author(s) -
Yevgeniy Bodyanskiy,
Olha Chala,
Natalia Kasatkina,
Iryna Pliss
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022374
Subject(s) - defuzzification , neuro fuzzy , softmax function , fuzzy logic , artificial intelligence , mathematics , fuzzy set operations , computer science , fuzzy classification , fuzzy number , mathematical optimization , fuzzy control system , algorithm , machine learning , fuzzy set , artificial neural network
In the paper, we propose the modified generalized neo-fuzzy system. It is designed to solve the pattern-image recognition task by working with data that are fed to the system in the image form. The neo-fuzzy system can work with small training datasets, where classes can overlap in a features space. The core of the system under consideration is a modification of multidimensional generalized neuro-fuzzy neuron with an additional softmax activation function in the output layer instead of the defuzzification layer and quartic-kernel functions as membership ones. The learning procedure of the system combined cross-entropy criterion optimization using a matrix version of the optimal by speed Kaczmarz-Widrow-Hoff algorithm with the additional filtering (smoothing) properties. In comparison to the well-known systems, the modified neo-fuzzy one provides both numerical and computational implementation simplicity. The computational experiments have proved the effectiveness of the modified generalized neo-fuzzy-neuron, including the situation with shot training datasets.