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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Published in:	IEEE transactions on fuzzy systems Vol. 28; no. 5; pp. 939 - 952
Main Authors:	Cao, Bin, Zhao, Jianwei, Lv, Zhihan, Gu, Yu, Yang, Peng, Halgamuge, Saman K.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.05.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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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 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.
AbstractList	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.
Author	Cao, Bin Lv, Zhihan Gu, Yu Yang, Peng Zhao, Jianwei Halgamuge, Saman K.
Author_xml	– sequence: 1 givenname: Bin orcidid: 0000-0003-4558-9501 surname: Cao fullname: Cao, Bin email: caobin@scse.hebut.edu.cn organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China – sequence: 2 givenname: Jianwei orcidid: 0000-0003-0424-4056 surname: Zhao fullname: Zhao, Jianwei email: 201422102003@stu.hebut.edu.cn organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China – sequence: 3 givenname: Zhihan orcidid: 0000-0003-2525-3074 surname: Lv fullname: Lv, Zhihan email: lvzhihan@gmail.com organization: School of Data Science and Software Engineering, Qingdao University, Qingdao, China – sequence: 4 givenname: Yu orcidid: 0000-0003-0073-1383 surname: Gu fullname: Gu, Yu email: guyu@mail.buct.edu.cn organization: Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, China – sequence: 5 givenname: Peng orcidid: 0000-0002-1129-8485 surname: Yang fullname: Yang, Peng email: yangp@hebut.edu.cn organization: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China – sequence: 6 givenname: Saman K. orcidid: 0000-0002-2536-4930 surname: Halgamuge fullname: Halgamuge, Saman K. email: saman@unimelb.edu.au organization: School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China
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Snippet	Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized...
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SubjectTerms	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
Title	Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction
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