Modified generalized neo-fuzzy system with combined online fast learning in medical diagnostic task for situations of information deficit

Yevgeniy Bodyanskiy; Olha Chala; Natalia Kasatkina; Iryna Pliss; Yevgeniy Bodyanskiy; Olha Chala; Natalia Kasatkina; Iryna Pliss

doi:10.3934/mbe.2022374

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 8: 8003-8018. doi: 10.3934/mbe.2022374

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Modified generalized neo-fuzzy system with combined online fast learning in medical diagnostic task for situations of information deficit

1.
Control systems research laboratory, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
2.
Artificial intelligence department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
3.
Division of doctoral and post-graduate, National University of Food Technology, Kyiv, Ukraine

Academic Editor: Ivan V. Izonin

Received: 01 February 2022 Revised: 12 April 2022 Accepted: 19 April 2022 Published: 30 May 2022

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.
- medical diagnostic,
- neo-fuzzy neuron,
- online learning,
- small training set,
- overlapping classes,
- membership functions
Citation: Yevgeniy Bodyanskiy, Olha Chala, Natalia Kasatkina, Iryna Pliss. Modified generalized neo-fuzzy system with combined online fast learning in medical diagnostic task for situations of information deficit[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 8003-8018. doi: 10.3934/mbe.2022374

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Abstract

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.

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