Rule-based fuzzy neural networks realized with the aid of linear function Prototype-driven fuzzy clustering and layer Reconstruction-based network design strategy
In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzz...
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| Vydané v: | Expert systems with applications Ročník 219; s. 119655 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.06.2023
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzzy c-regression model (FCRM) clustering unlike fuzzy c-means (FCM) clustering LFPFC represents the prototypes of clusters as linear functions, and this can lead to more reliable data analysis of complex regression problems. We propose two types of LFPFC such as an estimated output-based LFPFC and a distance-based LFPFC. The estimated output-based LFPFC uses the output estimated on a basis of the simple model instead of the target output to calculate the centroid of LFPFC. A centroid of distance-based LFPFC is computed through the Euclidean distance between input data and the centroid of the cluster. By using two kinds of LFPFC approaches, we propose three different types of fuzzy neural networks: i) the fuzzy neural networks through layer reconstruction-based network design strategy consists of two models. The first model serves as an estimate of the desired output and the estimated output is used in the LFPFC of the second model. ii) In the fuzzy neural networks applied to the basic architecture of distance-based LFPFC, the hidden layer using the membership function changes to basic distance-based LFPFC, and the partition matrix obtained from LFPFC is used as the output of the hidden layer. iii) in the fuzzy neural network with the advanced architecture of distance-based LFPFC, an additional auxiliary layer is considered between the hidden and output layers to estimate the membership function of output space through LFPFC. In the experiments, we evaluate the performance index of the proposed models using publicly available machine learning datasets. The superiority of the proposed fuzzy neural networks designed by using LFPFC is demonstrated through the comparative analysis with the diverse regression models offered in the Weka data mining software. By conducting the Friedman test we show that the proposed model exhibits visible competitiveness from the viewpoint of performance. In addition, a real-world Portland cement dataset is dealt with to demonstrate the superiority of the models designed with the aid of LFPFC and reinforced layer reconstruction-based network design strategy. |
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| AbstractList | In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzzy c-regression model (FCRM) clustering unlike fuzzy c-means (FCM) clustering LFPFC represents the prototypes of clusters as linear functions, and this can lead to more reliable data analysis of complex regression problems. We propose two types of LFPFC such as an estimated output-based LFPFC and a distance-based LFPFC. The estimated output-based LFPFC uses the output estimated on a basis of the simple model instead of the target output to calculate the centroid of LFPFC. A centroid of distance-based LFPFC is computed through the Euclidean distance between input data and the centroid of the cluster. By using two kinds of LFPFC approaches, we propose three different types of fuzzy neural networks: i) the fuzzy neural networks through layer reconstruction-based network design strategy consists of two models. The first model serves as an estimate of the desired output and the estimated output is used in the LFPFC of the second model. ii) In the fuzzy neural networks applied to the basic architecture of distance-based LFPFC, the hidden layer using the membership function changes to basic distance-based LFPFC, and the partition matrix obtained from LFPFC is used as the output of the hidden layer. iii) in the fuzzy neural network with the advanced architecture of distance-based LFPFC, an additional auxiliary layer is considered between the hidden and output layers to estimate the membership function of output space through LFPFC. In the experiments, we evaluate the performance index of the proposed models using publicly available machine learning datasets. The superiority of the proposed fuzzy neural networks designed by using LFPFC is demonstrated through the comparative analysis with the diverse regression models offered in the Weka data mining software. By conducting the Friedman test we show that the proposed model exhibits visible competitiveness from the viewpoint of performance. In addition, a real-world Portland cement dataset is dealt with to demonstrate the superiority of the models designed with the aid of LFPFC and reinforced layer reconstruction-based network design strategy. |
| ArticleNumber | 119655 |
| Author | Kim, Eun-Hu Pedrycz, Witold Park, Sang-Beom Oh, Sung-Kwun |
| Author_xml | – sequence: 1 givenname: Sang-Beom surname: Park fullname: Park, Sang-Beom email: sangbeom91@suwon.ac.kr organization: School of Electrical & Electronic Engineering, The University of Suwon, 17 Wauan-gil, Bongdam-eup, Hwaseong-si, Gyeonggi-do 18323, South Korea – sequence: 2 givenname: Sung-Kwun orcidid: 0000-0001-6798-8955 surname: Oh fullname: Oh, Sung-Kwun email: ohsk@suwon.ac.kr organization: School of Electrical & Electronic Engineering, The University of Suwon, 17 Wauan-gil, Bongdam-eup, Hwaseong-si, Gyeonggi-do 18323, South Korea – sequence: 3 givenname: Eun-Hu orcidid: 0000-0002-3636-1524 surname: Kim fullname: Kim, Eun-Hu email: wdkim@suwon.ac.kr organization: Research Center for Big Data and Artificial Intelligence, Linyi University, Linyi 276005, China – sequence: 4 givenname: Witold surname: Pedrycz fullname: Pedrycz, Witold email: wpedrycz@ualberta.ca organization: Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada |
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| Keywords | Distance-based LFPFC Estimated output-based LFPFC Linear function prototype-driven fuzzy clustering (LFPFC) Layer reconstruction-based network design strategy Fuzzy c-regression model (FCRM) clustering |
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| SubjectTerms | Distance-based LFPFC Estimated output-based LFPFC Fuzzy c-regression model (FCRM) clustering Layer reconstruction-based network design strategy Linear function prototype-driven fuzzy clustering (LFPFC) |
| Title | Rule-based fuzzy neural networks realized with the aid of linear function Prototype-driven fuzzy clustering and layer Reconstruction-based network design strategy |
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