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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| Vydáno v: | Expert systems with applications Ročník 219; s. 119655 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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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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| Cites_doi | 10.1016/j.knosys.2016.12.003 10.1016/j.knosys.2020.106467 10.4028/www.scientific.net/AMM.278-280.1323 10.1016/j.asoc.2020.106275 10.1016/j.knosys.2019.105229 10.1016/0098-3004(84)90020-7 10.1016/j.ins.2011.10.015 10.1016/j.eswa.2021.115761 10.1049/iet-cta:20060415 10.1109/TFUZZ.2017.2785244 10.1016/j.asoc.2021.107766 10.2478/v10006-012-0047-0 10.1109/TFUZZ.2017.2704542 10.1016/j.eswa.2020.113856 10.1080/03081079.2015.1072523 10.1109/TCYB.2016.2628182 10.1016/j.neucom.2020.11.029 10.1109/TFUZZ.2013.2286993 10.1109/91.413225 10.1109/TIP.2012.2226048 10.1109/TCYB.2016.2638861 10.1007/s11222-009-9153-8 10.1007/s00500-018-3265-z 10.1109/91.873580 10.1109/91.236552 10.1016/j.eswa.2020.113702 10.1109/TFUZZ.2004.840099 10.1016/C2009-0-19715-5 10.1016/j.fss.2014.12.004 10.1016/j.engappai.2009.02.003 10.1007/s40815-018-0497-0 10.1016/j.neunet.2018.03.018 10.1016/j.neucom.2021.06.047 10.1109/TCYB.2014.2382679 10.1080/01969727408546062 10.1016/j.knosys.2021.106750 10.1016/j.knosys.2009.12.002 10.1109/TNNLS.2017.2665581 10.1016/j.eswa.2010.07.112 |
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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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| References | (5), https://doi.org/3054-3068. 10.1109/TFUZZ.2017.2785244. Neville (b0130) 2012 Shi, Zhang, Zhang, Sun, Song (b0170) 2020; 191 Campos Souza (b0025) 2020; 92 Leski, Kotas (b0110) 2015; 279 Celik, Lee (b0030) 2012; 22 Kim, Oh, Pedrycz (b0100) 2018; 104 Askari, Saeed, Younas (b0010) 2020; 161 Qian, Sui (b0160) 2021; 186 Zhang, Oh, Pedrycz, Yang, Han (b0210) 2021; 112 Izakian, Abraham (b0080) 2011; 38 Pal, Pal, Keller, Bezdek (b0145) 2005; 13 Yeh, Su (b0195) 2016; 47 Parker, Hall (b0150) 2013; 22 Zhou, Wang, Huang, Liu (b0215) 2021; 213 Bezdek, Ehrlich, Full (b0015) 1984; 10 Huang, Oh, Pedrycz (b0075) 2021; 458 Dunn (b0045) 1974; 4 Chen, Liu, Xie, Zhang, Chen (b0035) 2016; 47 Pramod, Pillai (b0155) 2021; 215 Zarandi, Gamasaee, Turksen (b0205) 2012; 187 Yu, Song, Zhang (b0200) 2013; 278–280 , Güler Dincer (b0055) 2018; 20 Kung, Su (b0105) 2007; 1 Edition, Morgan Kaufmann. https://doi.org/10.1016/C2009-0-19715-5. Soltani, Chaari, Ben Hmida (b0175) 2012; 22 4 (3), 1104-1113. https://doi.org/ 10.1109/TFUZZ.2017.2704542. Fushiki (b0050) 2011; 21 Zou, W., Li, C., & Zhang, N. (2017). A T-S fuzzy model identification approach based on a modified inter type-2 FRCM algorithm. Wang, Er, Han (b0185) 2015; 45 Kim, Kim, Oh, Kim (b0085) 2017; 12 Lin, Lee (b0125) 1996 Soltani, Telmoudi, Chaouech, Ali, Chaari (b9000) 2019; 23 Kim, E.-H., Oh, S.-K., & Pedrycz, W. (2017c). Design of reinforced interval type-2 fuzzy c-means-based fuzzy classifier. Demšar (b0040) 2006; 7 Pal, Bezdek (b0140) 1995; 3 Li, Zhou, Xiang, Li, An (b0115) 2009; 22 Bezdek (b0020) 2013 Li, Yuan, Ruan, Chen, Luo (b0120) 2021; 427 Taherdoost (b0180) 2016; 5 Askari (b0005) 2021; 165 Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Hathaway, Bezdek, Hu (b0065) 2000; 8 Kim, Oh, Pedrycz (b0090) 2017; 119 Hathaway, Bezdek (b0060) 1993; 1 Roh, Oh, Pedrycz (b0165) 2010; 23 He, Dong (b0070) 2017; 29 Oh, Kim, Pedrycz (b0135) 2016; 45 Fushiki (10.1016/j.eswa.2023.119655_b0050) 2011; 21 Huang (10.1016/j.eswa.2023.119655_b0075) 2021; 458 Lin (10.1016/j.eswa.2023.119655_b0125) 1996 Dunn (10.1016/j.eswa.2023.119655_b0045) 1974; 4 Yeh (10.1016/j.eswa.2023.119655_b0195) 2016; 47 Pal (10.1016/j.eswa.2023.119655_b0145) 2005; 13 Askari (10.1016/j.eswa.2023.119655_b0010) 2020; 161 Kim (10.1016/j.eswa.2023.119655_b0085) 2017; 12 10.1016/j.eswa.2023.119655_b0220 Bezdek (10.1016/j.eswa.2023.119655_b0020) 2013 Yu (10.1016/j.eswa.2023.119655_b0200) 2013; 278–280 Kim (10.1016/j.eswa.2023.119655_b0090) 2017; 119 Zhou (10.1016/j.eswa.2023.119655_b0215) 2021; 213 Neville (10.1016/j.eswa.2023.119655_b0130) 2012 Askari (10.1016/j.eswa.2023.119655_b0005) 2021; 165 Pramod (10.1016/j.eswa.2023.119655_b0155) 2021; 215 He (10.1016/j.eswa.2023.119655_b0070) 2017; 29 Güler Dincer (10.1016/j.eswa.2023.119655_b0055) 2018; 20 Izakian (10.1016/j.eswa.2023.119655_b0080) 2011; 38 Li (10.1016/j.eswa.2023.119655_b0115) 2009; 22 Li (10.1016/j.eswa.2023.119655_b0120) 2021; 427 Qian (10.1016/j.eswa.2023.119655_b0160) 2021; 186 Soltani (10.1016/j.eswa.2023.119655_b0175) 2012; 22 Celik (10.1016/j.eswa.2023.119655_b0030) 2012; 22 10.1016/j.eswa.2023.119655_b0190 Parker (10.1016/j.eswa.2023.119655_b0150) 2013; 22 Roh (10.1016/j.eswa.2023.119655_b0165) 2010; 23 Shi (10.1016/j.eswa.2023.119655_b0170) 2020; 191 10.1016/j.eswa.2023.119655_b0095 Bezdek (10.1016/j.eswa.2023.119655_b0015) 1984; 10 Kung (10.1016/j.eswa.2023.119655_b0105) 2007; 1 Hathaway (10.1016/j.eswa.2023.119655_b0060) 1993; 1 Hathaway (10.1016/j.eswa.2023.119655_b0065) 2000; 8 Oh (10.1016/j.eswa.2023.119655_b0135) 2016; 45 Chen (10.1016/j.eswa.2023.119655_b0035) 2016; 47 Pal (10.1016/j.eswa.2023.119655_b0140) 1995; 3 Taherdoost (10.1016/j.eswa.2023.119655_b0180) 2016; 5 Kim (10.1016/j.eswa.2023.119655_b0100) 2018; 104 Soltani (10.1016/j.eswa.2023.119655_b9000) 2019; 23 Demšar (10.1016/j.eswa.2023.119655_b0040) 2006; 7 Campos Souza (10.1016/j.eswa.2023.119655_b0025) 2020; 92 Leski (10.1016/j.eswa.2023.119655_b0110) 2015; 279 Wang (10.1016/j.eswa.2023.119655_b0185) 2015; 45 Zarandi (10.1016/j.eswa.2023.119655_b0205) 2012; 187 Zhang (10.1016/j.eswa.2023.119655_b0210) 2021; 112 |
| References_xml | – reference: Edition, Morgan Kaufmann. https://doi.org/10.1016/C2009-0-19715-5. – volume: 8 start-page: 576 year: 2000 end-page: 582 ident: b0065 article-title: Generalized fuzzy c-means clustering strategies using L/sub p/norm distances publication-title: IEEE Transactions on Fuzzy Systems – volume: 22 start-page: 646 year: 2009 end-page: 653 ident: b0115 article-title: T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm publication-title: Engineering Applications of Artificial Intelligence – volume: 45 start-page: 434 year: 2016 end-page: 454 ident: b0135 article-title: Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: Design and analysis publication-title: International Journal of General Systems – volume: 23 start-page: 202 year: 2010 end-page: 219 ident: b0165 article-title: A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering publication-title: Knowledge-Based Systems – volume: 92 year: 2020 ident: b0025 article-title: Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature publication-title: Applied Soft Computing – volume: 23 start-page: 6125 year: 2019 end-page: 6134 ident: b9000 article-title: Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with noise and outliers publication-title: Soft Computing – volume: 20 start-page: 1872 year: 2018 end-page: 1887 ident: b0055 article-title: A new fuzzy time series model based on fuzzy C-regression model publication-title: International Journal of Fuzzy Systems – reference: . 4 – volume: 186 year: 2021 ident: b0160 article-title: A novel structural adaptive discrete grey prediction model and its application in forecasting renewable energy generation publication-title: Expert Systems with Applications – year: 2013 ident: b0020 article-title: Pattern recognition with fuzzy objective function algorithms publication-title: Springer Science & Business Media New York – volume: 22 start-page: 617 year: 2012 end-page: 628 ident: b0175 article-title: A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization publication-title: International Journal of Applied Mathematics and Computer Science – volume: 278–280 start-page: 1323 year: 2013 end-page: 1326 ident: b0200 article-title: Fuzzy c-regression models publication-title: Applied Mechanics and Materials – volume: 47 start-page: 2343 year: 2016 end-page: 2352 ident: b0195 article-title: Efficient approach for RLS type learning in TSK neural fuzzy systems publication-title: IEEE Transactions on Cybernetics – year: 2012 ident: b0130 article-title: Properties of Concrete – reference: (3), 1104-1113. https://doi.org/ 10.1109/TFUZZ.2017.2704542. – reference: (5), https://doi.org/3054-3068. 10.1109/TFUZZ.2017.2785244. – volume: 4 start-page: 1 year: 1974 end-page: 15 ident: b0045 article-title: Some recent investigations of a new fuzzy partitioning algorithm and its application to pattern classification problems publication-title: Journal of Cybernetics – volume: 10 start-page: 191 year: 1984 end-page: 203 ident: b0015 article-title: FCM: The fuzzy c-means clustering algorithm publication-title: Computers & Geosciences – volume: 112 year: 2021 ident: b0210 article-title: Building fuzzy relationships between compressive strength and 3D microstructural image features for cement hydration using Gaussian mixture model-based polynomial radial basis function neural networks publication-title: Applied Soft Computing – reference: Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). – reference: Zou, W., Li, C., & Zhang, N. (2017). A T-S fuzzy model identification approach based on a modified inter type-2 FRCM algorithm. – volume: 21 start-page: 137 year: 2011 end-page: 146 ident: b0050 article-title: Estimation of prediction error by using K-fold cross-validation publication-title: Statistics and Computing – volume: 191 year: 2020 ident: b0170 article-title: A fuzzy c-means algorithm based on the relationship among attributes of data and its application in tunnel boring machine publication-title: Knowledge-Based Systems – volume: 427 start-page: 29 year: 2021 end-page: 39 ident: b0120 article-title: A proportional-integral-derivative-incorporated stochastic gradient-based latent factor analysis model publication-title: Neurocomputing – reference: , – volume: 5 start-page: 18 year: 2016 end-page: 27 ident: b0180 article-title: Sampling methods in research methodology; how to choose a sampling technique for research publication-title: International Journal of Academic Research in Management – volume: 3 start-page: 370 year: 1995 end-page: 379 ident: b0140 article-title: On cluster validity for the fuzzy c-means model publication-title: IEEE Transactions on Fuzzy systems – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: b0040 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: The Journal of Machine Learning Research – volume: 29 start-page: 1174 year: 2017 end-page: 1186 ident: b0070 article-title: Adaptive fuzzy neural network control for a constrained robot using impedance learning publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 458 start-page: 454 year: 2021 end-page: 467 ident: b0075 article-title: Fuzzy reinforced polynomial neural networks constructed with the aid of PNN architecture and fuzzy hybrid predictor based on nonlinear function publication-title: Neurocomputing – volume: 119 start-page: 44 year: 2017 end-page: 58 ident: b0090 article-title: Reinforced rule-based fuzzy models: Design and analysis publication-title: Knowledge-Based Systems – volume: 1 start-page: 1255 year: 2007 end-page: 1265 ident: b0105 article-title: Affine Takagi-Sugeno fuzzy modelling algorithm by fuzzy c-regression models clustering with a novel cluster validity criterion publication-title: IET Control Theory & Applications – volume: 13 start-page: 517 year: 2005 end-page: 530 ident: b0145 article-title: A possibilistic fuzzy c-means clustering algorithm publication-title: IEEE Transactions on Fuzzy Systems – volume: 45 start-page: 2732 year: 2015 end-page: 2743 ident: b0185 article-title: Large tanker motion model identification using generalized ellipsoidal basis function-based fuzzy neural networks publication-title: IEEE Transactions on Cybernetics – volume: 12 start-page: 2388 year: 2017 end-page: 2398 ident: b0085 article-title: Design of robust face recognition system realized with the aid of automatic pose estimation-based classification and preprocessing networks structure publication-title: Journal of Electrical Engineering and Technology – reference: Kim, E.-H., Oh, S.-K., & Pedrycz, W. (2017c). Design of reinforced interval type-2 fuzzy c-means-based fuzzy classifier. – volume: 22 start-page: 1258 year: 2012 end-page: 1261 ident: b0030 article-title: Comments on “A robust fuzzy local information c-means clustering algorithm” publication-title: IEEE Transactions on Image Processing – volume: 22 start-page: 1229 year: 2013 end-page: 1244 ident: b0150 article-title: Accelerating fuzzy-c means using an estimated subsample size publication-title: IEEE Transactions on Fuzzy Systems – year: 1996 ident: b0125 article-title: Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems – volume: 47 start-page: 2448 year: 2016 end-page: 2459 ident: b0035 article-title: Asymptotic fuzzy neural network control for pure-feedback stochastic systems based on a semi-Nussbaum function technique publication-title: IEEE Transactions on Cybernetics – volume: 161 year: 2020 ident: b0010 article-title: Heap-based optimizer inspired by corporate rank hierarchy for global optimization publication-title: Expert Systems with Applications – volume: 104 start-page: 1 year: 2018 end-page: 14 ident: b0100 article-title: Design of double fuzzy clustering-driven context neural networks publication-title: Neural Networks – volume: 165 year: 2021 ident: b0005 article-title: Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development publication-title: Expert Systems with Applications – volume: 38 start-page: 1835 year: 2011 end-page: 1838 ident: b0080 article-title: Fuzzy C-means and fuzzy swarm for fuzzy clustering problem publication-title: Expert Systems with Applications – volume: 187 start-page: 179 year: 2012 end-page: 203 ident: b0205 article-title: A type-2 fuzzy c-regression clustering algorithm for Takagi-Sugeno system identification and its application in the steel industry publication-title: Information Sciences – volume: 215 year: 2021 ident: b0155 article-title: K-Means clustering based Extreme Learning ANFIS with improved interpretability for regression problems publication-title: Knowledge-Based Systems – volume: 1 start-page: 195 year: 1993 end-page: 204 ident: b0060 article-title: Switching regression models and fuzzy clustering publication-title: IEEE Transactions on Fuzzy Systems – volume: 279 start-page: 112 year: 2015 end-page: 129 ident: b0110 article-title: On robust fuzzy c-regression models publication-title: Fuzzy Sets and Systems – volume: 213 year: 2021 ident: b0215 article-title: Comparative study on the time series forecasting of web traffic based on statistical model and Generative Adversarial model publication-title: Knowledge-Based Systems – volume: 119 start-page: 44 year: 2017 ident: 10.1016/j.eswa.2023.119655_b0090 article-title: Reinforced rule-based fuzzy models: Design and analysis publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2016.12.003 – volume: 213 year: 2021 ident: 10.1016/j.eswa.2023.119655_b0215 article-title: Comparative study on the time series forecasting of web traffic based on statistical model and Generative Adversarial model publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2020.106467 – volume: 278–280 start-page: 1323 year: 2013 ident: 10.1016/j.eswa.2023.119655_b0200 article-title: Fuzzy c-regression models publication-title: Applied Mechanics and Materials doi: 10.4028/www.scientific.net/AMM.278-280.1323 – volume: 92 year: 2020 ident: 10.1016/j.eswa.2023.119655_b0025 article-title: Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106275 – volume: 191 year: 2020 ident: 10.1016/j.eswa.2023.119655_b0170 article-title: A fuzzy c-means algorithm based on the relationship among attributes of data and its application in tunnel boring machine publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.105229 – volume: 10 start-page: 191 issue: 2–3 year: 1984 ident: 10.1016/j.eswa.2023.119655_b0015 article-title: FCM: The fuzzy c-means clustering algorithm publication-title: Computers & Geosciences doi: 10.1016/0098-3004(84)90020-7 – volume: 187 start-page: 179 year: 2012 ident: 10.1016/j.eswa.2023.119655_b0205 article-title: A type-2 fuzzy c-regression clustering algorithm for Takagi-Sugeno system identification and its application in the steel industry publication-title: Information Sciences doi: 10.1016/j.ins.2011.10.015 – volume: 186 year: 2021 ident: 10.1016/j.eswa.2023.119655_b0160 article-title: A novel structural adaptive discrete grey prediction model and its application in forecasting renewable energy generation publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.115761 – volume: 1 start-page: 1255 issue: 5 year: 2007 ident: 10.1016/j.eswa.2023.119655_b0105 article-title: Affine Takagi-Sugeno fuzzy modelling algorithm by fuzzy c-regression models clustering with a novel cluster validity criterion publication-title: IET Control Theory & Applications doi: 10.1049/iet-cta:20060415 – ident: 10.1016/j.eswa.2023.119655_b0095 doi: 10.1109/TFUZZ.2017.2785244 – year: 2012 ident: 10.1016/j.eswa.2023.119655_b0130 – volume: 112 year: 2021 ident: 10.1016/j.eswa.2023.119655_b0210 article-title: Building fuzzy relationships between compressive strength and 3D microstructural image features for cement hydration using Gaussian mixture model-based polynomial radial basis function neural networks publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2021.107766 – year: 1996 ident: 10.1016/j.eswa.2023.119655_b0125 – volume: 22 start-page: 617 issue: 3 year: 2012 ident: 10.1016/j.eswa.2023.119655_b0175 article-title: A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization publication-title: International Journal of Applied Mathematics and Computer Science doi: 10.2478/v10006-012-0047-0 – ident: 10.1016/j.eswa.2023.119655_b0220 doi: 10.1109/TFUZZ.2017.2704542 – volume: 5 start-page: 18 issue: 2 year: 2016 ident: 10.1016/j.eswa.2023.119655_b0180 article-title: Sampling methods in research methodology; how to choose a sampling technique for research publication-title: International Journal of Academic Research in Management – year: 2013 ident: 10.1016/j.eswa.2023.119655_b0020 article-title: Pattern recognition with fuzzy objective function algorithms publication-title: Springer Science & Business Media New York – volume: 165 year: 2021 ident: 10.1016/j.eswa.2023.119655_b0005 article-title: Fuzzy C-Means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113856 – volume: 45 start-page: 434 issue: 4 year: 2016 ident: 10.1016/j.eswa.2023.119655_b0135 article-title: Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: Design and analysis publication-title: International Journal of General Systems doi: 10.1080/03081079.2015.1072523 – volume: 47 start-page: 2448 issue: 9 year: 2016 ident: 10.1016/j.eswa.2023.119655_b0035 article-title: Asymptotic fuzzy neural network control for pure-feedback stochastic systems based on a semi-Nussbaum function technique publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2016.2628182 – volume: 427 start-page: 29 year: 2021 ident: 10.1016/j.eswa.2023.119655_b0120 article-title: A proportional-integral-derivative-incorporated stochastic gradient-based latent factor analysis model publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.11.029 – volume: 22 start-page: 1229 issue: 5 year: 2013 ident: 10.1016/j.eswa.2023.119655_b0150 article-title: Accelerating fuzzy-c means using an estimated subsample size publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2013.2286993 – volume: 3 start-page: 370 issue: 3 year: 1995 ident: 10.1016/j.eswa.2023.119655_b0140 article-title: On cluster validity for the fuzzy c-means model publication-title: IEEE Transactions on Fuzzy systems doi: 10.1109/91.413225 – volume: 22 start-page: 1258 issue: 3 year: 2012 ident: 10.1016/j.eswa.2023.119655_b0030 article-title: Comments on “A robust fuzzy local information c-means clustering algorithm” publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2012.2226048 – volume: 7 start-page: 1 year: 2006 ident: 10.1016/j.eswa.2023.119655_b0040 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: The Journal of Machine Learning Research – volume: 47 start-page: 2343 issue: 9 year: 2016 ident: 10.1016/j.eswa.2023.119655_b0195 article-title: Efficient approach for RLS type learning in TSK neural fuzzy systems publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2016.2638861 – volume: 21 start-page: 137 issue: 2 year: 2011 ident: 10.1016/j.eswa.2023.119655_b0050 article-title: Estimation of prediction error by using K-fold cross-validation publication-title: Statistics and Computing doi: 10.1007/s11222-009-9153-8 – volume: 23 start-page: 6125 issue: 15 year: 2019 ident: 10.1016/j.eswa.2023.119655_b9000 article-title: Design of a robust interval-valued type-2 fuzzy c-regression model for a nonlinear system with noise and outliers publication-title: Soft Computing doi: 10.1007/s00500-018-3265-z – volume: 8 start-page: 576 issue: 5 year: 2000 ident: 10.1016/j.eswa.2023.119655_b0065 article-title: Generalized fuzzy c-means clustering strategies using L/sub p/norm distances publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/91.873580 – volume: 1 start-page: 195 issue: 3 year: 1993 ident: 10.1016/j.eswa.2023.119655_b0060 article-title: Switching regression models and fuzzy clustering publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/91.236552 – volume: 161 year: 2020 ident: 10.1016/j.eswa.2023.119655_b0010 article-title: Heap-based optimizer inspired by corporate rank hierarchy for global optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113702 – volume: 13 start-page: 517 issue: 4 year: 2005 ident: 10.1016/j.eswa.2023.119655_b0145 article-title: A possibilistic fuzzy c-means clustering algorithm publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2004.840099 – ident: 10.1016/j.eswa.2023.119655_b0190 doi: 10.1016/C2009-0-19715-5 – volume: 279 start-page: 112 year: 2015 ident: 10.1016/j.eswa.2023.119655_b0110 article-title: On robust fuzzy c-regression models publication-title: Fuzzy Sets and Systems doi: 10.1016/j.fss.2014.12.004 – volume: 22 start-page: 646 issue: 4–5 year: 2009 ident: 10.1016/j.eswa.2023.119655_b0115 article-title: T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2009.02.003 – volume: 12 start-page: 2388 issue: 6 year: 2017 ident: 10.1016/j.eswa.2023.119655_b0085 article-title: Design of robust face recognition system realized with the aid of automatic pose estimation-based classification and preprocessing networks structure publication-title: Journal of Electrical Engineering and Technology – volume: 20 start-page: 1872 issue: 6 year: 2018 ident: 10.1016/j.eswa.2023.119655_b0055 article-title: A new fuzzy time series model based on fuzzy C-regression model publication-title: International Journal of Fuzzy Systems doi: 10.1007/s40815-018-0497-0 – volume: 104 start-page: 1 year: 2018 ident: 10.1016/j.eswa.2023.119655_b0100 article-title: Design of double fuzzy clustering-driven context neural networks publication-title: Neural Networks doi: 10.1016/j.neunet.2018.03.018 – volume: 458 start-page: 454 year: 2021 ident: 10.1016/j.eswa.2023.119655_b0075 article-title: Fuzzy reinforced polynomial neural networks constructed with the aid of PNN architecture and fuzzy hybrid predictor based on nonlinear function publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.06.047 – volume: 45 start-page: 2732 issue: 12 year: 2015 ident: 10.1016/j.eswa.2023.119655_b0185 article-title: Large tanker motion model identification using generalized ellipsoidal basis function-based fuzzy neural networks publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2014.2382679 – volume: 4 start-page: 1 issue: 2 year: 1974 ident: 10.1016/j.eswa.2023.119655_b0045 article-title: Some recent investigations of a new fuzzy partitioning algorithm and its application to pattern classification problems publication-title: Journal of Cybernetics doi: 10.1080/01969727408546062 – volume: 215 year: 2021 ident: 10.1016/j.eswa.2023.119655_b0155 article-title: K-Means clustering based Extreme Learning ANFIS with improved interpretability for regression problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2021.106750 – volume: 23 start-page: 202 issue: 3 year: 2010 ident: 10.1016/j.eswa.2023.119655_b0165 article-title: A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2009.12.002 – volume: 29 start-page: 1174 issue: 4 year: 2017 ident: 10.1016/j.eswa.2023.119655_b0070 article-title: Adaptive fuzzy neural network control for a constrained robot using impedance learning publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2017.2665581 – volume: 38 start-page: 1835 issue: 3 year: 2011 ident: 10.1016/j.eswa.2023.119655_b0080 article-title: Fuzzy C-means and fuzzy swarm for fuzzy clustering problem publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.07.112 |
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