Efficient design of multicolumn RBF networks
To make radial basis function (RBF) networks efficient for large-scale learning tasks, the parallel technique provides a promising way for the construction of multicolumn RBF network (MCRN), where the task to be tackled is decomposed into several subtasks and RBF networks designed for these subtasks...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 450; S. 253 - 263 |
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25.08.2021
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | To make radial basis function (RBF) networks efficient for large-scale learning tasks, the parallel technique provides a promising way for the construction of multicolumn RBF network (MCRN), where the task to be tackled is decomposed into several subtasks and RBF networks designed for these subtasks are integrated as a parallel structure to obtain the final model. This paper focuses on presenting an efficient design method for MCRN based on improved generalized hybrid constructive (GHC) learning algorithm such that desired performance and high efficiency are guaranteed. Specifically, a minimum difference scheme is firstly introduced to divide a large-scale dataset into several parts with similar distribution. For each subtask, a compact RBF network is designed by improved GHC learning algorithm. By fully considering the inherent properties of RBF networks, a K-nearest potential centers based criterion is established to calculate the output of testing sample. Experiments on some benchmark classification problems are conducted to verify the advantages of the proposed method. In terms of training and testing efficiencies and classification accuracy, it is shown that our model is superior over some existing ones. |
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| AbstractList | To make radial basis function (RBF) networks efficient for large-scale learning tasks, the parallel technique provides a promising way for the construction of multicolumn RBF network (MCRN), where the task to be tackled is decomposed into several subtasks and RBF networks designed for these subtasks are integrated as a parallel structure to obtain the final model. This paper focuses on presenting an efficient design method for MCRN based on improved generalized hybrid constructive (GHC) learning algorithm such that desired performance and high efficiency are guaranteed. Specifically, a minimum difference scheme is firstly introduced to divide a large-scale dataset into several parts with similar distribution. For each subtask, a compact RBF network is designed by improved GHC learning algorithm. By fully considering the inherent properties of RBF networks, a K-nearest potential centers based criterion is established to calculate the output of testing sample. Experiments on some benchmark classification problems are conducted to verify the advantages of the proposed method. In terms of training and testing efficiencies and classification accuracy, it is shown that our model is superior over some existing ones. |
| Author | Qian, Xusheng Huang, Tingwen Han, Ziyang Huang, He |
| Author_xml | – sequence: 1 givenname: Ziyang surname: Han fullname: Han, Ziyang organization: School of Electronics and Information Engineering, Soochow University, Suzhou 215006, PR China – sequence: 2 givenname: Xusheng surname: Qian fullname: Qian, Xusheng organization: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, PR China – sequence: 3 givenname: He surname: Huang fullname: Huang, He email: hhuang@suda.edu.cn organization: School of Electronics and Information Engineering, Soochow University, Suzhou 215006, PR China – sequence: 4 givenname: Tingwen surname: Huang fullname: Huang, Tingwen organization: Texas A & M University at Qatar, Doha 5825, Qatar |
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| Cites_doi | 10.1109/TSE.1979.234200 10.1016/j.neunet.2015.12.011 10.1109/TNNLS.2018.2886341 10.1109/TNNLS.2017.2650865 10.1109/CVPR.2012.6248110 10.1109/TCYB.2015.2484378 10.1145/1961189.1961199 10.1109/TPAMI.2019.2906594 10.1109/TNNLS.2015.2497286 10.1109/TNN.2010.2045657 10.1109/TAC.1974.1100705 10.1109/TNNLS.2017.2716947 10.1109/TNNLS.2013.2295813 10.1109/TNN.2006.880583 10.1016/j.neunet.2017.07.004 10.1016/j.neucom.2020.04.105 10.1109/TCYB.2018.2869861 10.1109/TNNLS.2017.2731319 10.1109/TNNLS.2012.2185059 10.1016/j.ijepes.2012.08.014 10.1109/TNNLS.2014.2350957 10.1109/5.58326 10.1109/TCYB.2019.2921057 10.1016/S0893-6080(01)00027-2 10.1109/TIE.2011.2164773 10.1016/j.neunet.2015.05.001 10.1109/TCYB.2017.2764744 10.1109/TNNLS.2012.2226748 10.1109/TCYB.2018.2833805 10.1109/TNNLS.2019.2952000 10.1109/TNN.2009.2019270 10.1016/j.neucom.2016.09.027 |
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| Keywords | Generalized hybrid constructive algorithm Efficient learning Multicolumn RBF networks Parallel structure Potential centers Minimum difference scheme |
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| References | Rouhani, Javan (b0075) 2016; 75 J. Chen, Y. Wu, Y. Yang, S. Wen, K. Shi, A. Bermak, T. Huang, An efficient memristor-based circuit implementation of squeeze-and-excitation fully convolutional neural networks, IEEE Trans. Neural Netw. Learn. Syst., in press. Wilamowski, Yu (b0140) 2010; 21 Xie, Yu, Hewlett, Różycki, Wilamowski (b0105) 2012; 23 Hu, Wen, Zeng, Huang (b0165) 2017; 221 Raitoharju, Kiranyaz, Gabbouj (b0070) 2016; 27 J.L. Bentley, Multidimensional binary search trees in database applications, IEEE Trans. Softw. Eng. SE-5 (4) (1979) 333–340. Bai, Zhou, Li, Li (b0025) 2020; 50 S. Wen, J. Chen, Y. Wu, Z. Yan, Y. Yang, and T. Huang, CKFO: convolutional kernel first operated algorithm with applications in memristor-based convolutional neural networks, IEEE Trans. Comput. Aided Design Integr. Circuits Syst., in press. Yu, Reiner, Xie, Bartczak, Wilamowski (b0110) 2014; 25 Han, Wu, Zhang, Tian, Qiao (b0040) 2019; 49 Wang, Han, Shen, Li, Dong (b0175) 2020; 45 Poggio, Girosi (b0010) 1990; 78 Qian, Huang, Chen, Huang (b0085) 2017; 47 Bortman, Aladjem (b0115) 2009; 20 H. Akaike, A new look at the statistical model identification, IEEE Trans. Automat. Contr. AC-19 (6) (1974) 716–723. Yu, Xie, Paszczynski, Wilamowski (b0005) 2011; 58 Wang, Feng, Han, Leung (b0065) 2018; 29 Liang, Huang, Saratchandran, Sundararajan (b0100) 2006; 17 Wang, Han, Zhang, Lu (b0170) 2020; 403 A. Asuncion, D.J. Newman, UCI Machine Learning Repository, accessed on Mar. 3, 2016. [Online]. Available: http://www.ics.uci.edu//MLRepository.html. Wang, Liu, Li, Wang (b0015) 2018; 29 Hoori, Motai (b0145) 2018; 29 H. Ran, S. Wen, Q. Li, Y. Yang, K. Shi, Y. Feng, P. Zhou, T. Huang, Memristor-based edge computing of blaze block for image recognition, IEEE Trans. Neural Netw. Learn. Syst., in press. Bonab, Can (b0155) 2019; 30 Yoo, Oh, Pedrycz (b0030) 2015; 69 Seghouane, Shokouhi (b0050) 2020 Babu, Suresh (b0095) 2013; 24 D. Cireşan, U. Meier, J. Schmidhuber, Multi-column deep neural networks for image classification, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 3642–3649. Mesquita, Freitas, Gomes, Mattos (b0035) 2020; 31 Wu, Różycki, Wilamowski (b0120) 2015; 26 Qian, Huang, Chen, Huang (b0080) 2017; 94 Xie, Xie, Huang, Gui, Yang (b0020) 2018; 48 Schwenker, Kestler, Palm (b0090) 2001; 14 Chang, Lin (b0190) 2011; 2 Chen, Gong, Hong, Chen (b0045) 2016; 46 Chang, Huang (b0060) 2019; 49 Javan, Mashhadi, Rouhani (b0180) 2013; 44 Que, Belkin (b0055) 2020; 42 Javan (10.1016/j.neucom.2021.04.040_b0180) 2013; 44 Rouhani (10.1016/j.neucom.2021.04.040_b0075) 2016; 75 10.1016/j.neucom.2021.04.040_b0130 Mesquita (10.1016/j.neucom.2021.04.040_b0035) 2020; 31 10.1016/j.neucom.2021.04.040_b0195 Wang (10.1016/j.neucom.2021.04.040_b0015) 2018; 29 Hu (10.1016/j.neucom.2021.04.040_b0165) 2017; 221 10.1016/j.neucom.2021.04.040_b0135 Bai (10.1016/j.neucom.2021.04.040_b0025) 2020; 50 Bonab (10.1016/j.neucom.2021.04.040_b0155) 2019; 30 Han (10.1016/j.neucom.2021.04.040_b0040) 2019; 49 Seghouane (10.1016/j.neucom.2021.04.040_b0050) 2020 10.1016/j.neucom.2021.04.040_b0150 Chang (10.1016/j.neucom.2021.04.040_b0190) 2011; 2 Raitoharju (10.1016/j.neucom.2021.04.040_b0070) 2016; 27 Yu (10.1016/j.neucom.2021.04.040_b0110) 2014; 25 Wang (10.1016/j.neucom.2021.04.040_b0170) 2020; 403 Qian (10.1016/j.neucom.2021.04.040_b0080) 2017; 94 Hoori (10.1016/j.neucom.2021.04.040_b0145) 2018; 29 Chen (10.1016/j.neucom.2021.04.040_b0045) 2016; 46 Poggio (10.1016/j.neucom.2021.04.040_b0010) 1990; 78 Schwenker (10.1016/j.neucom.2021.04.040_b0090) 2001; 14 Liang (10.1016/j.neucom.2021.04.040_b0100) 2006; 17 Bortman (10.1016/j.neucom.2021.04.040_b0115) 2009; 20 10.1016/j.neucom.2021.04.040_b0185 Wang (10.1016/j.neucom.2021.04.040_b0065) 2018; 29 10.1016/j.neucom.2021.04.040_b0125 Yoo (10.1016/j.neucom.2021.04.040_b0030) 2015; 69 Yu (10.1016/j.neucom.2021.04.040_b0005) 2011; 58 Chang (10.1016/j.neucom.2021.04.040_b0060) 2019; 49 Qian (10.1016/j.neucom.2021.04.040_b0085) 2017; 47 Que (10.1016/j.neucom.2021.04.040_b0055) 2020; 42 10.1016/j.neucom.2021.04.040_b0160 Babu (10.1016/j.neucom.2021.04.040_b0095) 2013; 24 Wilamowski (10.1016/j.neucom.2021.04.040_b0140) 2010; 21 Xie (10.1016/j.neucom.2021.04.040_b0105) 2012; 23 Xie (10.1016/j.neucom.2021.04.040_b0020) 2018; 48 Wu (10.1016/j.neucom.2021.04.040_b0120) 2015; 26 Wang (10.1016/j.neucom.2021.04.040_b0175) 2020; 45 |
| References_xml | – volume: 27 start-page: 2458 year: 2016 end-page: 2471 ident: b0070 article-title: Training radial basis function neural networks for classification via class-specific clustering publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 2 start-page: 1 year: 2011 end-page: 27 ident: b0190 article-title: LIBSVM: a library for support vector machines publication-title: ACM Trans. Intell. Syst. Technol. – volume: 17 start-page: 1411 year: 2006 end-page: 1423 ident: b0100 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. – reference: H. Ran, S. Wen, Q. Li, Y. Yang, K. Shi, Y. Feng, P. Zhou, T. Huang, Memristor-based edge computing of blaze block for image recognition, IEEE Trans. Neural Netw. Learn. Syst., in press. – volume: 221 start-page: 24 year: 2017 end-page: 31 ident: b0165 article-title: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm publication-title: Neurocomputing – volume: 44 start-page: 988 year: 2013 end-page: 996 ident: b0180 article-title: A fast static security assessment method based on radial basis function neural networks using enhanced clustering publication-title: Int. J. Elect. Power Energy Syst. – volume: 58 start-page: 5438 year: 2011 end-page: 5450 ident: b0005 article-title: Advantages of radial basis function networks for dynamic system design publication-title: IEEE Trans. Ind. Elect. – reference: H. Akaike, A new look at the statistical model identification, IEEE Trans. Automat. Contr. AC-19 (6) (1974) 716–723. – volume: 49 start-page: 4460 year: 2019 end-page: 4472 ident: b0060 article-title: Automatic tuning of the RBF kernel parameter for batch-mode active learning algorithms: a scalable framework publication-title: IEEE Trans. Cybern. – volume: 49 start-page: 69 year: 2019 end-page: 82 ident: b0040 article-title: Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization publication-title: IEEE Trans. Cybern. – volume: 29 start-page: 3870 year: 2018 end-page: 3878 ident: b0065 article-title: ADMM-based algorithm for training fault tolerant RBF networks and selecting centers publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 26 start-page: 1659 year: 2015 end-page: 1668 ident: b0120 article-title: A hybrid constructive algorithm for single-Layer feedforward networks learning publication-title: IEEE Trans. Neural Netw. Learn. Syst. – reference: J.L. Bentley, Multidimensional binary search trees in database applications, IEEE Trans. Softw. Eng. SE-5 (4) (1979) 333–340. – reference: A. Asuncion, D.J. Newman, UCI Machine Learning Repository, accessed on Mar. 3, 2016. [Online]. Available: http://www.ics.uci.edu//MLRepository.html. – volume: 78 start-page: 1481 year: 1990 end-page: 1497 ident: b0010 article-title: Networks for approximation and learning publication-title: Proc. IEEE – volume: 94 start-page: 239 year: 2017 end-page: 254 ident: b0080 article-title: Efficient construction of sparse radial basis function neural networks using publication-title: Neural Netw. – reference: S. Wen, J. Chen, Y. Wu, Z. Yan, Y. Yang, and T. Huang, CKFO: convolutional kernel first operated algorithm with applications in memristor-based convolutional neural networks, IEEE Trans. Comput. Aided Design Integr. Circuits Syst., in press. – volume: 20 start-page: 1039 year: 2009 end-page: 1045 ident: b0115 article-title: A growing and pruning method for radial basis function networks publication-title: IEEE Trans. Neural Network – volume: 403 start-page: 237 year: 2020 end-page: 246 ident: b0170 article-title: An SAE-based resampling SVM ensemble learning paradigm for pipeline leakage detection publication-title: Neurocomputing – volume: 42 start-page: 1856 year: 2020 end-page: 1867 ident: b0055 article-title: Back to the future: Radial basis function network revisited publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 47 start-page: 3634 year: 2017 end-page: 3648 ident: b0085 article-title: Generalized hybrid constructive learning algorithm for multioutput RBF networks publication-title: IEEE Trans. Cybern. – reference: D. Cireşan, U. Meier, J. Schmidhuber, Multi-column deep neural networks for image classification, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2012, pp. 3642–3649. – volume: 24 start-page: 194 year: 2013 end-page: 206 ident: b0095 article-title: Sequential projection-based metacognitive learning in a radial basis function network for classification problems publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 23 start-page: 609 year: 2012 end-page: 619 ident: b0105 article-title: Fast and efficient second-order method for training radial basis function networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 31 start-page: 4389 year: 2020 end-page: 4399 ident: b0035 article-title: LS-SVR as a Bayesian RBF network publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 48 start-page: 3313 year: 2018 end-page: 3322 ident: b0020 article-title: Coordinated optimization for the descent gradient of technical index in the iron removal process publication-title: IEEE Trans. Cybern. – reference: J. Chen, Y. Wu, Y. Yang, S. Wen, K. Shi, A. Bermak, T. Huang, An efficient memristor-based circuit implementation of squeeze-and-excitation fully convolutional neural networks, IEEE Trans. Neural Netw. Learn. Syst., in press. – year: 2020 ident: b0050 article-title: Adaptive learning for robust radial basis function networks publication-title: IEEE Trans. Cybern., in press – volume: 75 start-page: 150 year: 2016 end-page: 161 ident: b0075 article-title: Two fast and accurate heuristic RBF learning rules for data classification publication-title: Neural Netw. – volume: 29 start-page: 766 year: 2018 end-page: 778 ident: b0145 article-title: Multicolumn RBF network publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 46 start-page: 2683 year: 2016 end-page: 2692 ident: b0045 article-title: A fast adaptive tunable RBF network for nonstationary systems publication-title: IEEE Trans. Cybern. – volume: 21 start-page: 930 year: 2010 end-page: 937 ident: b0140 article-title: Improved computation for Levenberg-Marquardt traning publication-title: IEEE Trans. Neural Netw. – volume: 14 start-page: 439 year: 2001 end-page: 458 ident: b0090 article-title: Three learning phases for radial-basis-function networks publication-title: Neural Netw. – volume: 45 start-page: 1 year: 2020 end-page: 13 ident: b0175 article-title: Full-information particle swarm optimizer based on event-triggering strategy and its applications publication-title: Acta Autom. Sin. – volume: 30 start-page: 2735 year: 2019 end-page: 2745 ident: b0155 article-title: Less is more: a comprehensive framework for the number of components of ensemble classifiers publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 25 start-page: 1793 year: 2014 end-page: 1803 ident: b0110 article-title: An incremental design of radial basis function networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 69 start-page: 111 year: 2015 end-page: 125 ident: b0030 article-title: Optimized face recognition algorithm using radial basis function neural networks and its practical applications publication-title: Neural Netw. – volume: 29 start-page: 3658 year: 2018 end-page: 3668 ident: b0015 article-title: Adaptive neural output-feedback control for a class of nonlower triangular nonlinear systems with unmodeled dynamics publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 50 start-page: 3433 year: 2020 end-page: 3443 ident: b0025 article-title: Adaptive reinforcement learning neural network control for uncertain nonlinear system with input saturation publication-title: IEEE Trans. Cybern. – ident: 10.1016/j.neucom.2021.04.040_b0195 – ident: 10.1016/j.neucom.2021.04.040_b0160 doi: 10.1109/TSE.1979.234200 – volume: 75 start-page: 150 year: 2016 ident: 10.1016/j.neucom.2021.04.040_b0075 article-title: Two fast and accurate heuristic RBF learning rules for data classification publication-title: Neural Netw. doi: 10.1016/j.neunet.2015.12.011 – volume: 30 start-page: 2735 issue: 9 year: 2019 ident: 10.1016/j.neucom.2021.04.040_b0155 article-title: Less is more: a comprehensive framework for the number of components of ensemble classifiers publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2018.2886341 – volume: 29 start-page: 766 issue: 4 year: 2018 ident: 10.1016/j.neucom.2021.04.040_b0145 article-title: Multicolumn RBF network publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2017.2650865 – ident: 10.1016/j.neucom.2021.04.040_b0150 doi: 10.1109/CVPR.2012.6248110 – ident: 10.1016/j.neucom.2021.04.040_b0130 – ident: 10.1016/j.neucom.2021.04.040_b0135 – volume: 46 start-page: 2683 issue: 12 year: 2016 ident: 10.1016/j.neucom.2021.04.040_b0045 article-title: A fast adaptive tunable RBF network for nonstationary systems publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2015.2484378 – volume: 2 start-page: 1 issue: 27 year: 2011 ident: 10.1016/j.neucom.2021.04.040_b0190 article-title: LIBSVM: a library for support vector machines publication-title: ACM Trans. Intell. Syst. Technol. doi: 10.1145/1961189.1961199 – volume: 42 start-page: 1856 issue: 8 year: 2020 ident: 10.1016/j.neucom.2021.04.040_b0055 article-title: Back to the future: Radial basis function network revisited publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2019.2906594 – volume: 27 start-page: 2458 issue: 12 year: 2016 ident: 10.1016/j.neucom.2021.04.040_b0070 article-title: Training radial basis function neural networks for classification via class-specific clustering publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2497286 – ident: 10.1016/j.neucom.2021.04.040_b0125 – volume: 21 start-page: 930 issue: 6 year: 2010 ident: 10.1016/j.neucom.2021.04.040_b0140 article-title: Improved computation for Levenberg-Marquardt traning publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2010.2045657 – ident: 10.1016/j.neucom.2021.04.040_b0185 doi: 10.1109/TAC.1974.1100705 – volume: 47 start-page: 3634 issue: 11 year: 2017 ident: 10.1016/j.neucom.2021.04.040_b0085 article-title: Generalized hybrid constructive learning algorithm for multioutput RBF networks publication-title: IEEE Trans. Cybern. – volume: 29 start-page: 3658 issue: 8 year: 2018 ident: 10.1016/j.neucom.2021.04.040_b0015 article-title: Adaptive neural output-feedback control for a class of nonlower triangular nonlinear systems with unmodeled dynamics publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2017.2716947 – volume: 25 start-page: 1793 issue: 10 year: 2014 ident: 10.1016/j.neucom.2021.04.040_b0110 article-title: An incremental design of radial basis function networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2013.2295813 – volume: 17 start-page: 1411 issue: 6 year: 2006 ident: 10.1016/j.neucom.2021.04.040_b0100 article-title: A fast and accurate online sequential learning algorithm for feedforward networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2006.880583 – volume: 94 start-page: 239 year: 2017 ident: 10.1016/j.neucom.2021.04.040_b0080 article-title: Efficient construction of sparse radial basis function neural networks using L1-regularization publication-title: Neural Netw. doi: 10.1016/j.neunet.2017.07.004 – year: 2020 ident: 10.1016/j.neucom.2021.04.040_b0050 article-title: Adaptive learning for robust radial basis function networks publication-title: IEEE Trans. Cybern., in press – volume: 403 start-page: 237 year: 2020 ident: 10.1016/j.neucom.2021.04.040_b0170 article-title: An SAE-based resampling SVM ensemble learning paradigm for pipeline leakage detection publication-title: Neurocomputing doi: 10.1016/j.neucom.2020.04.105 – volume: 49 start-page: 4460 issue: 12 year: 2019 ident: 10.1016/j.neucom.2021.04.040_b0060 article-title: Automatic tuning of the RBF kernel parameter for batch-mode active learning algorithms: a scalable framework publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2869861 – volume: 29 start-page: 3870 issue: 8 year: 2018 ident: 10.1016/j.neucom.2021.04.040_b0065 article-title: ADMM-based algorithm for training fault tolerant RBF networks and selecting centers publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2017.2731319 – volume: 23 start-page: 609 issue: 4 year: 2012 ident: 10.1016/j.neucom.2021.04.040_b0105 article-title: Fast and efficient second-order method for training radial basis function networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2012.2185059 – volume: 44 start-page: 988 issue: 1 year: 2013 ident: 10.1016/j.neucom.2021.04.040_b0180 article-title: A fast static security assessment method based on radial basis function neural networks using enhanced clustering publication-title: Int. J. Elect. Power Energy Syst. doi: 10.1016/j.ijepes.2012.08.014 – volume: 26 start-page: 1659 issue: 8 year: 2015 ident: 10.1016/j.neucom.2021.04.040_b0120 article-title: A hybrid constructive algorithm for single-Layer feedforward networks learning publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2014.2350957 – volume: 78 start-page: 1481 issue: 9 year: 1990 ident: 10.1016/j.neucom.2021.04.040_b0010 article-title: Networks for approximation and learning publication-title: Proc. IEEE doi: 10.1109/5.58326 – volume: 50 start-page: 3433 issue: 8 year: 2020 ident: 10.1016/j.neucom.2021.04.040_b0025 article-title: Adaptive reinforcement learning neural network control for uncertain nonlinear system with input saturation publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2019.2921057 – volume: 14 start-page: 439 year: 2001 ident: 10.1016/j.neucom.2021.04.040_b0090 article-title: Three learning phases for radial-basis-function networks publication-title: Neural Netw. doi: 10.1016/S0893-6080(01)00027-2 – volume: 45 start-page: 1 year: 2020 ident: 10.1016/j.neucom.2021.04.040_b0175 article-title: Full-information particle swarm optimizer based on event-triggering strategy and its applications publication-title: Acta Autom. Sin. – volume: 58 start-page: 5438 issue: 12 year: 2011 ident: 10.1016/j.neucom.2021.04.040_b0005 article-title: Advantages of radial basis function networks for dynamic system design publication-title: IEEE Trans. Ind. Elect. doi: 10.1109/TIE.2011.2164773 – volume: 69 start-page: 111 year: 2015 ident: 10.1016/j.neucom.2021.04.040_b0030 article-title: Optimized face recognition algorithm using radial basis function neural networks and its practical applications publication-title: Neural Netw. doi: 10.1016/j.neunet.2015.05.001 – volume: 49 start-page: 69 issue: 1 year: 2019 ident: 10.1016/j.neucom.2021.04.040_b0040 article-title: Self-organizing RBF neural network using an adaptive gradient multiobjective particle swarm optimization publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2017.2764744 – volume: 24 start-page: 194 issue: 2 year: 2013 ident: 10.1016/j.neucom.2021.04.040_b0095 article-title: Sequential projection-based metacognitive learning in a radial basis function network for classification problems publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2012.2226748 – volume: 48 start-page: 3313 issue: 12 year: 2018 ident: 10.1016/j.neucom.2021.04.040_b0020 article-title: Coordinated optimization for the descent gradient of technical index in the iron removal process publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2833805 – volume: 31 start-page: 4389 issue: 10 year: 2020 ident: 10.1016/j.neucom.2021.04.040_b0035 article-title: LS-SVR as a Bayesian RBF network publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2019.2952000 – volume: 20 start-page: 1039 issue: 6 year: 2009 ident: 10.1016/j.neucom.2021.04.040_b0115 article-title: A growing and pruning method for radial basis function networks publication-title: IEEE Trans. Neural Network doi: 10.1109/TNN.2009.2019270 – volume: 221 start-page: 24 year: 2017 ident: 10.1016/j.neucom.2021.04.040_b0165 article-title: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.027 |
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| SubjectTerms | Efficient learning Generalized hybrid constructive algorithm Minimum difference scheme Multicolumn RBF networks Parallel structure Potential centers |
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