Journal article

Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction

B Cao, J Zhao, Z Lv, Y Gu, P Yang, SK Halgamuge

IEEE Transactions on Fuzzy Systems | Institute of Electrical and Electronics Engineers | Published : 2020

Abstract

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy se..

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University of Melbourne Researchers

Grants

Awarded by National Natural Science Foundation of China (NSFC)


Awarded by Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University


Funding Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61976242, and in part by the Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University under Grant 2018002.