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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Vydáno v:IEEE transactions on fuzzy systems Ročník 28; číslo 5; s. 939 - 952
Hlavní autoři: Cao, Bin, Zhao, Jianwei, Lv, Zhihan, Gu, Yu, Yang, Peng, Halgamuge, Saman K.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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 set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of prediction precision and network simplicity , each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions, and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance the optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements the existing fuzzy rough neural network and the long short-term memory network.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.2972207