{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T00:37:31Z","timestamp":1769215051949,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012576","name":"major projects of guangdong education department for foundation research and applied research","doi-asserted-by":"publisher","award":["2017KTSCX113"],"award-info":[{"award-number":["2017KTSCX113"]}],"id":[{"id":"10.13039\/501100012576","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10845-021-01897-7","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T18:02:40Z","timestamp":1642010560000},"page":"1779-1794","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1651-9099","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[]},{"given":"Shubin","family":"Lyu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5451-7230","authenticated-orcid":false,"given":"C. L. Philip","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Huimin","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-1735","authenticated-orcid":false,"given":"Zhengchun","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Pingsheng","family":"Quan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"1897_CR1","unstructured":"Arthur, D., & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. In SODA \u201907."},{"key":"1897_CR2","unstructured":"Asuncion, A., & Newman, D. (2007). Uci machine learning repository."},{"issue":"7","key":"1897_CR3","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/34.598228","volume":"19","author":"PN Belhumeur","year":"1997","unstructured":"Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711\u2013720.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1897_CR4","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","volume":"29","author":"C Chen","year":"2018","unstructured":"Chen, C., & Liu, Z. (2018). Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 29, 10\u201324.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1897_CR5","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/TNNLS.2018.2866622","volume":"30","author":"C Chen","year":"2019","unstructured":"Chen, C., Liu, Z., & Feng, S. (2019). Universal approximation capability of broad learning system and its structural variations. IEEE Transactions on Neural Networks and Learning Systems, 30, 1191\u20131204.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"1897_CR6","doi-asserted-by":"crossref","unstructured":"Elhefnawy, M., Ragab, A., & Ouali, M. S. (2021). Fault classification in the process industry using polygon generation and deep learning. Journal of Intelligent Manufacturing, 1\u201314.","DOI":"10.1007\/s10845-021-01742-x"},{"key":"1897_CR7","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/TCYB.2018.2857815","volume":"50","author":"S Feng","year":"2020","unstructured":"Feng, S., & Chen, C. (2020). Fuzzy broad learning system: A novel neuro-fuzzy model for regression and classification. IEEE Transactions on Cybernetics, 50, 414\u2013424.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"1897_CR8","first-page":"1","volume":"70","author":"Y Fu","year":"2021","unstructured":"Fu, Y., Cao, H., & Chen, X. (2021). Adaptive broad learning system for high-efficiency fault diagnosis of rotating machinery. IEEE Transactions on Instrumentation and Measurement, 70, 1\u201311.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1897_CR9","unstructured":"Gong, X., Zhang, T., Chen, C. L. P., & Liu, Z. (2021). Research review for broad learning system: Algorithms, theory, and applications. IEEE transactions on cybernetics PP."},{"key":"1897_CR10","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132\u20137141).","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"2","key":"1897_CR11","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"GB Huang","year":"2011","unstructured":"Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513\u2013529.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)"},{"key":"1897_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.neucom.2021.08.052","volume":"463","author":"S Huang","year":"2021","unstructured":"Huang, S., Liu, Z., Jin, W., & Mu, Y. (2021). Broad learning system with manifold regularized sparse features for semi-supervised classification. Neurocomputing, 463, 133\u2013143.","journal-title":"Neurocomputing"},{"key":"1897_CR13","unstructured":"Janczak, A. (2004). Identification of nonlinear systems using neural networks and polynomial models: a block-oriented approach (Vol. 310). Springer."},{"key":"1897_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-017-9421-3","volume":"61","author":"J Jin","year":"2018","unstructured":"Jin, J., Liu, Z., & Chen, C. (2018). Discriminative graph regularized broad learning system for image recognition. Science China Information Sciences, 61, 1\u201314.","journal-title":"Science China Information Sciences"},{"issue":"5","key":"1897_CR15","doi-asserted-by":"publisher","first-page":"3054","DOI":"10.1109\/TFUZZ.2017.2785244","volume":"26","author":"EH Kim","year":"2017","unstructured":"Kim, E. H., Oh, S. K., & Pedrycz, W. (2017). Design of reinforced interval type-2 fuzzy c-means-based fuzzy classifier. IEEE Transactions on Fuzzy Systems, 26(5), 3054\u20133068.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"1897_CR16","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.neucom.2021.02.094","volume":"448","author":"S Li","year":"2021","unstructured":"Li, S., Xing, X., Fan, W., Cai, B., Fordson, P., & Xu, X. (2021). Spatiotemporal and frequential cascaded attention networks for speech emotion recognition. Neurocomputing, 448, 238\u2013248.","journal-title":"Neurocomputing"},{"key":"1897_CR17","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.neucom.2019.10.059","volume":"380","author":"J Lin","year":"2020","unstructured":"Lin, J., Liu, Z., Chen, C., & Zhang, Y. (2020). Quaternion broad learning system: A novel multi-dimensional filter for estimation and elimination tremor in teleoperation. Neurocomputing, 380, 78\u201386.","journal-title":"Neurocomputing"},{"key":"1897_CR18","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/TSMC.2020.3043147","volume":"51","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Chen, C., Feng, S., Feng, Q., & Zhang, T. (2021). Stacked broad learning system: From incremental flatted structure to deep model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 209\u2013222.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"1897_CR19","doi-asserted-by":"crossref","unstructured":"Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, pp. 1\u201310.","DOI":"10.1007\/s10845-021-01750-x"},{"key":"1897_CR20","doi-asserted-by":"crossref","unstructured":"Nikolaev, N. Y., & Iba, H. (2002). Genetic programming of polynomial models for financial forecasting. In Genetic Algorithms and Genetic Programming in Computational Finance (pp. 103\u2013123). Springer.","DOI":"10.1007\/978-1-4615-0835-9_5"},{"key":"1897_CR21","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.fss.2010.08.007","volume":"163","author":"SK Oh","year":"2011","unstructured":"Oh, S. K., Kim, W., Pedrycz, W., & Park, B. (2011). Polynomial-based radial basis function neural networks (p-rbf nns) realized with the aid of particle swarm optimization. Fuzzy Sets and Systems, 163, 54\u201377.","journal-title":"Fuzzy Sets and Systems"},{"issue":"1","key":"1897_CR22","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.neucom.2011.06.031","volume":"78","author":"SK Oh","year":"2012","unstructured":"Oh, S. K., Kim, W. D., Pedrycz, W., & Joo, S. C. (2012). Design of k-means clustering-based polynomial radial basis function neural networks (prbf nns) realized with the aid of particle swarm optimization and differential evolution. Neurocomputing, 78(1), 121\u2013132.","journal-title":"Neurocomputing"},{"key":"1897_CR23","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1016\/j.eswa.2012.08.046","volume":"40","author":"SK Oh","year":"2013","unstructured":"Oh, S. K., Yoo, S., & Pedrycz, W. (2013). Design of face recognition algorithm using pca -lda combined for hybrid data pre-processing and polynomial-based rbf neural networks\u202f: Design and its application. Expert Systems with Applications, 40, 1451\u20131466.","journal-title":"Expert Systems with Applications"},{"key":"1897_CR24","doi-asserted-by":"crossref","unstructured":"Ouyang, C. S., Kao, T. C., Cheng, Y. Y., Wu, C. H., Tsai, C. H., & Wu, M. W. (2016). An improved fuzzy extreme learning machine for classification and regression. In 2016 International Conference on Cybernetics, Robotics and Control (CRC), pp. 91\u201394. IEEE.","DOI":"10.1109\/CRC.2016.028"},{"issue":"2","key":"1897_CR25","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1016\/j.asoc.2007.07.013","volume":"8","author":"A Quteishat","year":"2008","unstructured":"Quteishat, A., & Lim, C. P. (2008). A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification. Applied Soft Computing, 8(2), 985\u2013995.","journal-title":"Applied Soft Computing"},{"key":"1897_CR26","doi-asserted-by":"crossref","unstructured":"Samaria, F. S., & Harter, A. C. (1994). Parameterisation of a stochastic model for human face identification. In Proceedings of 1994 IEEE workshop on applications of computer vision, pp. 138\u2013142. IEEE.","DOI":"10.1109\/ACV.1994.341300"},{"key":"1897_CR27","doi-asserted-by":"publisher","first-page":"6074","DOI":"10.1109\/TSMC.2019.2957818","volume":"51","author":"H Tang","year":"2021","unstructured":"Tang, H., Dong, P., & Shi, Y. (2021). A construction of robust representations for small data sets using broad learning system. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 6074\u20136084.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"1897_CR28","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1109\/TNNLS.2014.2341655","volume":"26","author":"SY Wong","year":"2015","unstructured":"Wong, S. Y., Yap, K. S., Yap, H. J., Tan, S. C., & Chang, S. W. (2015). On equivalence of fis and elm for interpretable rule-based knowledge representation. IEEE Transactions on Neural Networks and Learning Systems, 26, 1417\u20131430.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"1897_CR29","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1007\/s10845-020-01701-y","volume":"32","author":"D Yao","year":"2021","unstructured":"Yao, D., Liu, H., Yang, J., & Zhang, J. (2021). Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural network with the attention mechanism. Journal of Intelligent Manufacturing, 32(3), 729\u2013743.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"1897_CR30","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1007\/s12559-019-09698-0","volume":"12","author":"L Zhu","year":"2019","unstructured":"Zhu, L., Lian, C., Zeng, Z., & Su, Y. (2019). A broad learning system with ensemble and classification methods for multi-step-ahead wind speed prediction. Cognitive Computation, 12, 654\u2013666.","journal-title":"Cognitive Computation"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01897-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-021-01897-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-021-01897-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T17:13:54Z","timestamp":1678900434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-021-01897-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,12]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["1897"],"URL":"https:\/\/doi.org\/10.1007\/s10845-021-01897-7","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,12]]},"assertion":[{"value":"11 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}