Random Polynomial Neural Networks: Analysis and Design

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dc.authoridXiao, Yueyue/0009-0006-9606-2282
dc.authorid, Wei/0000-0002-4315-8487
dc.authorwosidZHU, LIEHUANG/A-6174-2018
dc.contributor.authorHuang, Wei
dc.contributor.authorXiao, Yueyue
dc.contributor.authorOh, Sung-Kwun
dc.contributor.authorPedrycz, Witold
dc.contributor.authorZhu, Liehuang
dc.date.accessioned2024-05-19T14:46:10Z
dc.date.available2024-05-19T14:46:10Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractIn this article, we propose the concept of random polynomial neural networks (RPNNs) realized based on the architecture of polynomial neural networks (PNNs) with random polynomial neurons (RPNs). RPNs exhibit generalized polynomial neurons (PNs) based on random forest (RF) architecture. In the design of RPNs, the target variables are no longer directly used in conventional decision trees, and the polynomial of these target variables is exploited here to determine the average prediction. Unlike the conventional performance index used in the selection of PNs, the correlation coefficient is adopted here to select the RPNs of each layer. When compared with the conventional PNs used in PNNs, the proposed RPNs exhibit the following advantages: first, RPNs are insensitive to outliers; second, RPNs can obtain the importance of each input variable after training; third, RPNs can alleviate the overfitting problem with the use of an RF structure. The overall nonlinearity of a complex system is captured by means of PNNs. Moreover, particle swarm optimization (PSO) is exploited to optimize the parameters when constructing RPNNs. The RPNNs take advantage of both RF and PNNs: it exhibits high accuracy based on ensemble learning used in the RF and is beneficial to describe high-order nonlinear relations between input and output variables stemming from PNNs. Experimental results based on a series of well-known modeling benchmarks illustrate that the proposed RPNNs outperform other state-of-the-art models reported in the literature.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [62227805, 62232002, 61872041]; Natural Science Foundation of Tianjin for Distinguished Young Scholars [19JCJQJC61500]; National Research Foundation of Korea (NRF) - Korea Government [Ministry of Science and Information and Communications Technology (MSIT)] [NRF-2021R1F1A1056102]en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 62227805, Grant 62232002, and Grant 61872041; in part by the Natural Science Foundation of Tianjin for Distinguished Young Scholars under Grant 19JCJQJC61500; and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea Government [Ministry of Science and Information and Communications Technology (MSIT)] under Grant NRF-2021R1F1A1056102. (Corresponding author: Sung-Kwun Oh.)en_US
dc.identifier.doi10.1109/TNNLS.2023.3288577
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.pmid37402196en_US
dc.identifier.urihttps://doi.org10.1109/TNNLS.2023.3288577
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5458
dc.identifier.wosWOS:001030839000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Neural Networks and Learning Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectParticle Swarm Optimization (Pso)en_US
dc.subjectPolynomial Neural Networks (Pnns)en_US
dc.subjectRandom Polynomial Neural Networks (Rpnns)en_US
dc.subjectRandom Polynomial Neurons (Rpns)en_US
dc.titleRandom Polynomial Neural Networks: Analysis and Designen_US
dc.typeArticleen_US

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