TSK Fuzzy Neural Network Use for COVID-19 Classification

Authors

DOI:

https://doi.org/10.18372/1990-5548.71.16825

Keywords:

Takagi–Sugeno–Kang fuzzy system, fuzzy neural networks ensemble, batch normalization

Abstract

It is considered t the Takagi-Sugeno-Kang fuzzy neural network and its modern variations. The use of regularization, random exclusion of rules from the rule base allows solving the problem of excessive similarity of rules in the rule base. The use of batch normalization to increase the generalizing properties of the network allows to increase the accuracy of the model, while maintaining the possibility of interpreting the results, which is characteristic of fuzzy neural networks. It is proposed to use an ensemble of fuzzy neural networks to increase the generalizing capabilities of the network. Studies of the Takagi-Sugeno-Kang fuzzy neural network for the task of diagnosing the coronavirus disease show that the proposed model works well and allows to improve the result.

Author Biography

Nataliia Shapoval , National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Candidate of Science (Engineering)

References

I. O. Kravets, and A. Yu. Timoshyn, "Exploring fuzzy neural networks for forecasting rapidly changing non-stationary time series," Scientific works [of the Petro Mohyla Black Sea State University]. Ser.: Computer technologies 191, vol. 179, 2012, pp. 72–79. [in Ukrainian]

Natalia Shovgun, "Fuzzy Neural Networks for Evaluating the Creditworthiness of the Borrowers," Information Theories & Applications, vol. 21, pp. 54–59, 2014.

O. Komandyrov, P. Kulikov, V. Ploskyi, and B. Yeremenko, "Application of the Takaga–Sugeno–Kanga Artificial Neuro-fussy Network to Assessment of the Technical Condition of Building Objects," Management of the development of complex systems, vol. 42, pp. 107–112, 2020. [in Ukrainian]

M. Nilashi, O. bin Ibrahim, N. Ithnin, and N. H. Sarmin, "A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS," Electronic Commerce Research and Applications, vol. 14(6), pp. 542–562, 2015.

N. V. Shovgun, "Increasing the effectiveness of fuzzy neural networks in the task of assessing the borrower's creditworthiness," Bulletin of NTUU "KPI". Informatics, management and computer technology, vol. 58, pp. 89–94, 2013. [in Ukrainian]

Y. Cui, D. Wu, & J. Huang, "Optimize TSK fuzzy systems for classification problems: Minibatch gradient descent with uniform regularization and batch normalization," IEEE Transactions on Fuzzy Systems, vol. 28(12), 2020, pp. 3065–3075.

D. Wu, Y. Yuan, J. Huang, & Y. Tan, "Optimize TSK fuzzy systems for regression problems: Minibatch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA)," IEEE Transactions on Fuzzy Systems, vol. 28(5), 2019, pp. 1003–1015.

V. Sineglazov, & J. Smirnova, "Intelligence Diagnostics of Heart Disease Based on Neural Networks Ensemble Use," Electronics and Control Systems, vol. 3(69), 2021, pp. 9–15.

S. Zhang, M. Liu, & J. Yan, "The diversified ensemble neural network," Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 16001–16011.

https://www.kaggle.com/code/midouazerty/symptoms-covid-19-using-7-machine-learning-98/data (accessed 15.07.22)

Downloads

Published

2022-06-27

Issue

Section

TELECOMMUNICATIONS AND RADIO ENGINEERING