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

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 i...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 28; H. 5; S. 939 - 952
Hauptverfasser:	Cao, Bin, Zhao, Jianwei, Lv, Zhihan, Gu, Yu, Yang, Peng, Halgamuge, Saman K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial neural networks Biological neural networks Computing time Distributed parallelism Evolutionary algorithms evolutionary neural network Fuzzy logic fuzzy rough neural network (FRNN) Fuzzy sets Mathematical models multiobjective evolution Multiple objective analysis Network management systems Neural networks Neurons Optimization Parameters Rough sets stock price prediction Task complexity
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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