{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T11:02:52Z","timestamp":1740135772765,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T00:00:00Z","timestamp":1617926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61773087"],"award-info":[{"award-number":["61773087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["DUT20LAB114","DUT2018TB06"],"award-info":[{"award-number":["DUT20LAB114","DUT2018TB06"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s00034-021-01691-z","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T09:09:02Z","timestamp":1617959342000},"page":"4346-4369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive Sparse Quantization Kernel Least Mean Square Algorithm for Online Prediction of Chaotic Time Series"],"prefix":"10.1007","volume":"40","author":[{"given":"Chaochao","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Weijie","family":"Ren","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2964-4884","authenticated-orcid":false,"given":"Min","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"issue":"3","key":"1691_CR1","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1090\/S0002-9947-1950-0051437-7","volume":"68","author":"N Aronszajn","year":"1950","unstructured":"N. Aronszajn, Theory of reproducing kernels. Trans. Am. Math. Soc. 68(3), 337\u2013404 (1950)","journal-title":"Trans. Am. Math. Soc."},{"key":"1691_CR2","doi-asserted-by":"crossref","unstructured":"B. Chen, W. Liu, J.C. Principe, Theoretical methods in machine learning, in Springer Handbook of Computational Intelligence, (Springer, 2015), pp. 523\u2013543","DOI":"10.1007\/978-3-662-43505-2_30"},{"issue":"1","key":"1691_CR3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/TNNLS.2011.2178446","volume":"23","author":"B Chen","year":"2011","unstructured":"B. Chen, S. Zhao, P. Zhu, J.C. Pr\u00edncipe, Quantized kernel least mean square algorithm. IEEE Trans. Neural Netw. Learn. Syst. 23(1), 22\u201332 (2011)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1691_CR4","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.ins.2018.06.045","volume":"496","author":"J Chen","year":"2019","unstructured":"J. Chen, K. Li, H. Rong, K. Bilal, K. Li, S.Y. Philip, A periodicity-based parallel time series prediction algorithm in cloud computing environments. Inf. Sci. 496, 506\u2013537 (2019)","journal-title":"Inf. Sci."},{"issue":"8","key":"1691_CR5","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1109\/TSP.2004.830985","volume":"52","author":"Y Engel","year":"2004","unstructured":"Y. Engel, S. Mannor, R. Meir, The kernel recursive least-squares algorithm. IEEE Trans. Signal Process. 52(8), 2275\u20132285 (2004)","journal-title":"IEEE Trans. Signal Process."},{"issue":"6","key":"1691_CR6","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.1016\/j.eswa.2012.10.046","volume":"40","author":"H Fan","year":"2013","unstructured":"H. Fan, Q. Song, A sparse kernel algorithm for online time series data prediction. Expert Syst. Appl. 40(6), 2174\u20132181 (2013)","journal-title":"Expert Syst. Appl."},{"key":"1691_CR7","doi-asserted-by":"crossref","unstructured":"J. Fernandez-Bes, V. Elvira, S. Van\u00a0Vaerenbergh, A probabilistic least-mean-squares filter, in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (IEEE, 2015), pp 2199\u20132203","DOI":"10.1109\/ICASSP.2015.7178361"},{"issue":"11","key":"1691_CR8","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1109\/TSP.2014.2318132","volume":"62","author":"W Gao","year":"2014","unstructured":"W. Gao, J. Chen, C. Richard, J. Huang, Online dictionary learning for kernel lms. IEEE Trans. Signal Process. 62(11), 2765\u20132777 (2014)","journal-title":"IEEE Trans. Signal Process."},{"issue":"5","key":"1691_CR9","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1109\/TCYB.2018.2816657","volume":"49","author":"M Han","year":"2018","unstructured":"M. Han, W. Ren, M. Xu, T. Qiu, Nonuniform state space reconstruction for multivariate chaotic time series. IEEE Transa. Cybern. 49(5), 1885\u20131895 (2018)","journal-title":"IEEE Transa. Cybern."},{"issue":"4","key":"1691_CR10","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TCYB.2018.2789686","volume":"49","author":"M Han","year":"2019","unstructured":"M. Han, S. Zhang, M. Xu, T. Qiu, N. Wang, Multivariate chaotic time series online prediction based on improved kernel recursive least squares algorithm. IEEE Trans. Cybern. 49(4), 1160\u20131172 (2019)","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"1691_CR11","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1109\/TNNLS.2012.2188414","volume":"23","author":"D Li","year":"2012","unstructured":"D. Li, M. Han, J. Wang, Chaotic time series prediction based on a novel robust echo state network. IEEE Trans. Neural Netw. Learn. Syst. 23(5), 787\u2013799 (2012)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"1691_CR12","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/TNNLS.2015.2418323","volume":"27","author":"K Li","year":"2016","unstructured":"K. Li, J.C. Pr\u00edncipe, The kernel adaptive autoregressive-moving-average algorithm. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 334\u2013346 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1691_CR13","doi-asserted-by":"publisher","first-page":"104785","DOI":"10.1016\/j.knosys.2019.05.028","volume":"181","author":"Y Li","year":"2019","unstructured":"Y. Li, Z. Zhu, D. Kong, H. Han, Y. Zhao, Ea-lstm: Evolutionary attention-based LSTM for time series prediction. Knowledge-Based Systems 181, 104785 (2019)","journal-title":"Knowledge-Based Systems"},{"key":"1691_CR14","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1016\/j.ymssp.2016.02.056","volume":"76","author":"J Liu","year":"2016","unstructured":"J. Liu, E. Zio, An adaptive online learning approach for support vector regression: Online-SVR-FID. Mech. Syst. Signal Process. 76, 796\u2013809 (2016)","journal-title":"Mech. Syst. Signal Process."},{"issue":"12","key":"1691_CR15","doi-asserted-by":"publisher","first-page":"1950","DOI":"10.1109\/TNN.2009.2033676","volume":"20","author":"W Liu","year":"2009","unstructured":"W. Liu, I. Park, J.C. Principe, An information theoretic approach of designing sparse kernel adaptive filters. IEEE Trans. Neural Netw. 20(12), 1950\u20131961 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"2","key":"1691_CR16","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1109\/TSP.2007.907881","volume":"56","author":"W Liu","year":"2008","unstructured":"W. Liu, P.P. Pokharel, J.C. Principe, The kernel least-mean-square algorithm. IEEE Trans. Signal Process. 56(2), 543\u2013554 (2008)","journal-title":"IEEE Trans. Signal Process."},{"key":"1691_CR17","first-page":"1","volume":"2008","author":"W Liu","year":"2008","unstructured":"W. Liu, J.C. Principe, Kernel affine projection algorithms. EURASIP J. Adv. Signal Process. 2008, 1\u201312 (2008)","journal-title":"EURASIP J. Adv. Signal Process."},{"issue":"3","key":"1691_CR18","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MCI.2010.937329","volume":"5","author":"W Liu","year":"2010","unstructured":"W. Liu, J.C. Principe, S. Haykin, Kernel adaptive filtering: A comprehensive introduction. IEEE Comput. Intell. Mag. 5(3), 52\u201355 (2010)","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"1","key":"1691_CR19","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s00034-018-0862-0","volume":"38","author":"Y Liu","year":"2019","unstructured":"Y. Liu, C. Sun, S. Jiang, A reduced gaussian kernel least-mean-square algorithm for nonlinear adaptive signal processing. Circuits Syst. Signal Process. 38(1), 371\u2013394 (2019)","journal-title":"Circuits Syst. Signal Process."},{"issue":"8","key":"1691_CR20","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1109\/TCYB.2017.2738060","volume":"48","author":"X Lu","year":"2018","unstructured":"X. Lu, L. Ming, W. Liu, H. Li, Probabilistic regularized extreme learning machine for robust modeling of noise data. IEEE Trans. Cybern. 48(8), 2368\u20132377 (2018)","journal-title":"IEEE Trans. Cybern."},{"key":"1691_CR21","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.engappai.2016.11.010","volume":"58","author":"W Ma","year":"2017","unstructured":"W. Ma, J. Duan, W. Man, H. Zhao, B. Chen, Robust kernel adaptive filters based on mean p-power error for noisy chaotic time series prediction. Eng. Appl. Artif. Intell. 58, 101\u2013110 (2017)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"04","key":"1691_CR22","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1142\/S0218127491000634","volume":"1","author":"L Noakes","year":"1991","unstructured":"L. Noakes, The takens embedding theorem. Int. J. Bifurc. Chaos 1(04), 867\u2013872 (1991)","journal-title":"Int. J. Bifurc. Chaos"},{"issue":"6","key":"1691_CR23","doi-asserted-by":"publisher","first-page":"3183","DOI":"10.1109\/TWC.2014.042314.131432","volume":"13","author":"K Pelekanakis","year":"2014","unstructured":"K. Pelekanakis, M. Chitre, Adaptive sparse channel estimation under symmetric alpha-stable noise. IEEE Trans. Wirel. Commun. 13(6), 3183\u20133195 (2014)","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"1691_CR24","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1991.3.2.213","volume-title":"A Resource-Allocating Network for Function Interpolation","author":"J Platt","year":"1991","unstructured":"J. Platt, A Resource-Allocating Network for Function Interpolation (MIT Press, Cambridge, 1991)"},{"issue":"3","key":"1691_CR25","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1109\/TSP.2008.2009895","volume":"57","author":"C Richard","year":"2008","unstructured":"C. Richard, J.C.M. Bermudez, P. Honeine, Online prediction of time series data with kernels. IEEE Trans. Signal Process. 57(3), 1058\u20131067 (2008)","journal-title":"IEEE Trans. Signal Process."},{"key":"1691_CR26","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.bspc.2018.05.002","volume":"45","author":"RR Sharma","year":"2018","unstructured":"R.R. Sharma, R.B. Pachori, Baseline wander and power line interference removal from ecg signals using eigenvalue decomposition. Biomed. Signal Process. Control 45, 33\u201349 (2018)","journal-title":"Biomed. Signal Process. Control"},{"issue":"8","key":"1691_CR27","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.1007\/s00034-018-0834-4","volume":"37","author":"RR Sharma","year":"2018","unstructured":"R.R. Sharma, R.B. Pachori, Eigenvalue decomposition of hankel matrix-based time-frequency representation for complex signals. Circuits Syst. Signal Process. 37(8), 3313\u20133329 (2018)","journal-title":"Circuits Syst. Signal Process."},{"key":"1691_CR28","doi-asserted-by":"crossref","unstructured":"F. Sheikholeslami, D. Berberidis, G.B. Giannakis, Kernel-based low-rank feature extraction on a budget for big data streams, in 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), (2015), pp 928\u2013932","DOI":"10.1109\/GlobalSIP.2015.7418333"},{"issue":"7\u20139","key":"1691_CR29","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.1016\/j.neucom.2008.05.010","volume":"72","author":"C Vairappan","year":"2009","unstructured":"C. Vairappan, H. Tamura, S. Gao, Z. Tang, Batch type local search-based adaptive neuro-fuzzy inference system (ANFIS) with self-feedbacks for time-series prediction. Neurocomputing 72(7\u20139), 1870\u20131877 (2009)","journal-title":"Neurocomputing"},{"key":"1691_CR30","doi-asserted-by":"crossref","unstructured":"S. Van\u00a0Vaerenbergh, I. Santamar\u00eda, W. Liu, J.C. Pr\u00edncipe, Fixed-budget kernel recursive least-squares, in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, (IEEE 2010) pp 1882\u20131885","DOI":"10.1109\/ICASSP.2010.5495350"},{"key":"1691_CR31","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"2013","unstructured":"V. Vapnik, The Nature of Statistical Learning Theory (Springer science & business media, Berlin, 2013)"},{"issue":"2","key":"1691_CR32","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1007\/s00034-019-01116-y","volume":"39","author":"L Wang","year":"2020","unstructured":"L. Wang, R. Liu, Human activity recognition based on wearable sensor using hierarchical deep LSTM networks. Circuits Syst. Signal Process. 39(2), 837\u2013856 (2020)","journal-title":"Circuits Syst. Signal Process."},{"issue":"7","key":"1691_CR33","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.1007\/s00034-018-1006-2","volume":"38","author":"S Wang","year":"2019","unstructured":"S. Wang, W. Wang, L. Dang, J. Yunxiang, Kernel least mean square based on the nystrom method. Circuits Syst. Signal Process. 38(7), 3133\u20133151 (2019)","journal-title":"Circuits Syst. Signal Process."},{"issue":"9","key":"1691_CR34","doi-asserted-by":"publisher","first-page":"4672","DOI":"10.1109\/TSP.2012.2200889","volume":"60","author":"M Yukawa","year":"2012","unstructured":"M. Yukawa, Multikernel adaptive filtering. IEEE Trans. Signal Process. 60(9), 4672\u20134682 (2012)","journal-title":"IEEE Trans. Signal Process."},{"issue":"1","key":"1691_CR35","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1109\/TBDATA.2018.2871151","volume":"6","author":"P Zhang","year":"2020","unstructured":"P. Zhang, Y. Jia, J. Gao, W. Song, H. Leung, Short-term rainfall forecasting using multi-layer perceptron. IEEE Trans. Big Data 6(1), 93\u2013106 (2020)","journal-title":"IEEE Trans. Big Data"},{"issue":"11","key":"1691_CR36","doi-asserted-by":"publisher","first-page":"5277","DOI":"10.1109\/TNNLS.2018.2795719","volume":"29","author":"S Zhang","year":"2018","unstructured":"S. Zhang, H. Cao, S. Yang, Y. Zhang, X. Hei, Sequential outlier criterion for sparsification of online adaptive filtering. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5277\u20135291 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"1691_CR37","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.1016\/j.sigpro.2013.02.012","volume":"93","author":"S Zhao","year":"2013","unstructured":"S. Zhao, B. Chen, P. Zhu, J.C. Pr\u00edncipe, Fixed budget quantized kernel least-mean-square algorithm. Signal Process. 93(9), 2759\u20132770 (2013)","journal-title":"Signal Process."},{"key":"1691_CR38","doi-asserted-by":"crossref","unstructured":"Z. Zhao, M. Jin, The decorrelated kernel least-mean-square algorithm, in 2016 IEEE 13th International Conference on Signal Processing (ICSP), (IEEE 2016), pp 367\u2013371","DOI":"10.1109\/ICSP.2016.7877857"},{"key":"1691_CR39","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.ymssp.2018.03.047","volume":"111","author":"H Zhou","year":"2018","unstructured":"H. Zhou, J. Huang, L. Feng, J. Thiyagalingam, T. Kirubarajan, Echo state kernel recursive least squares algorithm for machine condition prediction. Mech. Syst. Signal Process. 111, 68\u201386 (2018)","journal-title":"Mech. Syst. Signal Process."},{"issue":"5","key":"1691_CR40","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1109\/TSP.2011.2109956","volume":"59","author":"H Zhu","year":"2011","unstructured":"H. Zhu, G. Leus, G.B. Giannakis, Sparsity-cognizant total least-squares for perturbed compressive sampling. IEEE Trans. Signal Process. 59(5), 2002\u20132016 (2011)","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-021-01691-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-021-01691-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-021-01691-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T17:14:39Z","timestamp":1628097279000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-021-01691-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,9]]},"references-count":40,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["1691"],"URL":"https:\/\/doi.org\/10.1007\/s00034-021-01691-z","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"type":"print","value":"0278-081X"},{"type":"electronic","value":"1531-5878"}],"subject":[],"published":{"date-parts":[[2021,4,9]]},"assertion":[{"value":"20 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}