{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:22:25Z","timestamp":1743106945603,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62173063"],"award-info":[{"award-number":["62173063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00034-022-02258-2","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T15:05:16Z","timestamp":1672326316000},"page":"2698-2722","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Kernel Generalized Half-Quadratic Correntropy Conjugate Gradient Algorithm for Online Prediction of Chaotic Time Series"],"prefix":"10.1007","volume":"42","author":[{"given":"Huijuan","family":"Xia","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":[[2022,12,29]]},"reference":[{"key":"2258_CR1","unstructured":"F. Albu, K. Nishikawa, The kernel proportionate NLMS algorithm, in 21st European Signal Processing Conference (EUSIPCO 2013) (IEEE, 2013), pp. 1\u20135"},{"key":"2258_CR2","doi-asserted-by":"crossref","unstructured":"F. Albu, K. Nishikawa, A fixed budget implementation of a new variable step size kernel proportionate NLMS algorithm, in 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014) (IEEE, 2014), pp. 890\u2013894","DOI":"10.1109\/ICCAS.2014.6987907"},{"key":"2258_CR3","doi-asserted-by":"crossref","unstructured":"F. Albu, K. Nishikawa, New iterative kernel algorithms for nonlinear acoustic echo cancellation, in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (IEEE, 2015), pp. 734\u2013739","DOI":"10.1109\/APSIPA.2015.7415369"},{"key":"2258_CR4","doi-asserted-by":"crossref","unstructured":"F. Albu, K. Nishikawa, Low complexity kernel affine projection-type algorithms with a coherence criterion, in 2017 International Conference on Signals and Systems (ICSigSys) (IEEE, 2017), pp. 87\u201391","DOI":"10.1109\/ICSIGSYS.2017.7967076"},{"issue":"2","key":"2258_CR5","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1109\/78.823968","volume":"48","author":"PS Chang","year":"2000","unstructured":"P.S. Chang, A.N. Willson, Analysis of conjugate gradient algorithms for adaptive filtering. IEEE Trans. Signal Process. 48(2), 409\u2013418 (2000)","journal-title":"IEEE Trans. Signal Process."},{"issue":"13","key":"2258_CR6","doi-asserted-by":"publisher","first-page":"3376","DOI":"10.1109\/TSP.2016.2539127","volume":"64","author":"B Chen","year":"2016","unstructured":"B. Chen, L. Xing, H. Zhao, N. Zheng, J.C. Pr\u0131 et al., Generalized correntropy for robust adaptive filtering. IEEE Trans. Signal Process. 64(13), 3376\u20133387 (2016)","journal-title":"IEEE Trans. Signal Process."},{"issue":"2","key":"2258_CR7","doi-asserted-by":"publisher","first-page":"136","DOI":"10.3390\/sym11020136","volume":"11","author":"I Dassios","year":"2019","unstructured":"I. Dassios, Analytic loss minimization: theoretical framework of a second order optimization method. Symmetry 11(2), 136 (2019)","journal-title":"Symmetry"},{"issue":"10","key":"2258_CR8","doi-asserted-by":"publisher","first-page":"7884","DOI":"10.1002\/mma.5410","volume":"44","author":"I Dassios","year":"2021","unstructured":"I. Dassios, D. Baleanu, Optimal solutions for singular linear systems of caputo fractional differential equations. Math. Methods Appl. Sci. 44(10), 7884\u20137896 (2021)","journal-title":"Math. Methods Appl. Sci."},{"issue":"6","key":"2258_CR9","doi-asserted-by":"publisher","first-page":"A2783","DOI":"10.1137\/141002062","volume":"37","author":"I Dassios","year":"2015","unstructured":"I. Dassios, K. Fountoulakis, J. Gondzio, A preconditioner for a primal-dual newton conjugate gradient method for compressed sensing problems. SIAM J. Sci. Comput. 37(6), A2783\u2013A2812 (2015)","journal-title":"SIAM J. Sci. Comput."},{"issue":"8","key":"2258_CR10","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."},{"key":"2258_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113668","volume":"160","author":"S Garcia-Vega","year":"2020","unstructured":"S. Garcia-Vega, X. Zeng, J. Keane, Stock returns prediction using kernel adaptive filtering within a stock market interdependence approach. Expert Syst. Appl. 160, 113668 (2020)","journal-title":"Expert Syst. Appl."},{"key":"2258_CR12","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/TSP.2019.2952057","volume":"68","author":"Y He","year":"2020","unstructured":"Y. He, F. Wang, Y. Li, J. Qin, B. Chen, Robust matrix completion via maximum correntropy criterion and half-quadratic optimization. IEEE Trans. Signal Process. 68, 181\u2013195 (2020)","journal-title":"IEEE Trans. Signal Process."},{"issue":"7","key":"2258_CR13","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1109\/LSP.2018.2797079","volume":"25","author":"AR Heravi","year":"2018","unstructured":"A.R. Heravi, G.A. Hodtani, A new information theoretic relation between minimum error entropy and maximum correntropy. IEEE Signal Process. Lett. 25(7), 921\u2013925 (2018)","journal-title":"IEEE Signal Process. Lett."},{"issue":"10","key":"2258_CR14","first-page":"1252","volume":"64","author":"F Huang","year":"2017","unstructured":"F. Huang, J. Zhang, S. Zhang, Maximum versoria criterion-based robust adaptive filtering algorithm. IEEE Trans. Circuits Syst. II Express Briefs 64(10), 1252\u20131256 (2017)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"4","key":"2258_CR15","doi-asserted-by":"publisher","first-page":"1725","DOI":"10.1007\/s00034-016-0373-9","volume":"36","author":"A Khalili","year":"2017","unstructured":"A. Khalili, A. Rastegarnia, M.K. Islam, T.Y. Rezaii, Steady-state tracking analysis of adaptive filter with maximum correntropy criterion. Circuits Syst. Signal Process. 36(4), 1725\u20131734 (2017)","journal-title":"Circuits Syst. Signal Process."},{"issue":"4","key":"2258_CR16","first-page":"1","volume":"40","author":"MK Khandani","year":"2021","unstructured":"M.K. Khandani, W.B. Mikhael, Effect of sparse representation of time series data on learning rate of time-delay neural networks. Circuits Syst. Signal Process. 40(4), 1\u201326 (2021)","journal-title":"Circuits Syst. Signal Process."},{"issue":"5","key":"2258_CR17","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."},{"key":"2258_CR18","first-page":"1","volume":"41","author":"D Liu","year":"2021","unstructured":"D. Liu, H. Zhao, X. He, L. Zhou, Polynomial constraint generalized maximum correntropy normalized subband adaptive filter algorithm. Circuits Syst. Signal Process. 41, 1\u201318 (2021)","journal-title":"Circuits Syst. Signal Process."},{"issue":"11","key":"2258_CR19","doi-asserted-by":"publisher","first-page":"5286","DOI":"10.1109\/TSP.2007.896065","volume":"55","author":"W Liu","year":"2007","unstructured":"W. Liu, P.P. Pokharel, J.C. Principe, Correntropy: properties and applications in non-Gaussian signal processing. IEEE Trans. Signal Process. 55(11), 5286\u20135298 (2007)","journal-title":"IEEE Trans. Signal Process."},{"issue":"2","key":"2258_CR20","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":"2258_CR21","volume-title":"Kernel Adaptive Filtering: A Comprehensive Introduction","author":"W Liu","year":"2011","unstructured":"W. Liu, J.C. Principe, S. Haykin, Kernel Adaptive Filtering: A Comprehensive Introduction (Wiley, New York, 2011)"},{"key":"2258_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2021.108364","volume":"191","author":"X Liu","year":"2022","unstructured":"X. Liu, C. Song, Z. Pang, Kernel recursive maximum correntropy with variable center. Signal Process. 191, 108364 (2022)","journal-title":"Signal Process."},{"issue":"4","key":"2258_CR23","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TASSP.1987.1165167","volume":"35","author":"VJ Mathews","year":"1987","unstructured":"V.J. Mathews, S.H. Cho, Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm. IEEE Trans. Acoust. Speech Signal Process. 35(4), 450\u2013454 (1987)","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"issue":"1","key":"2258_CR24","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1145\/355984.355989","volume":"8","author":"CC Paige","year":"1982","unstructured":"C.C. Paige, M.A. Saunders, LSQR: an algorithm for sparse linear equations and sparse least squares. ACM Trans. Math. Softw. (TOMS) 8(1), 43\u201371 (1982)","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"issue":"5","key":"2258_CR25","doi-asserted-by":"publisher","first-page":"955","DOI":"10.3390\/sym14050955","volume":"14","author":"B Ramadevi","year":"2022","unstructured":"B. Ramadevi, K. Bingi, Chaotic time series forecasting approaches using machine learning techniques: a review. Symmetry 14(5), 955 (2022)","journal-title":"Symmetry"},{"issue":"3","key":"2258_CR26","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":"2258_CR27","unstructured":"S. Ruder, An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)"},{"key":"2258_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2020.107329","volume":"166","author":"S Sankar","year":"2020","unstructured":"S. Sankar, A. Kar, S. Burra, M. Swamy, V. Mladenovic, Nonlinear acoustic echo cancellation with kernelized adaptive filters. Appl. Acoust. 166, 107329 (2020)","journal-title":"Appl. Acoust."},{"issue":"4","key":"2258_CR29","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1109\/LSP.2010.2040203","volume":"17","author":"T Shao","year":"2010","unstructured":"T. Shao, Y.R. Zheng, J. Benesty, An affine projection sign algorithm robust against impulsive interferences. IEEE Signal Process. Lett. 17(4), 327\u2013330 (2010)","journal-title":"IEEE Signal Process. Lett."},{"key":"2258_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103797","volume":"95","author":"T Shen","year":"2020","unstructured":"T. Shen, W. Ren, M. Han, Quantized generalized maximum correntropy criterion based kernel recursive least squares for online time series prediction. Eng. Appl. Artif. Intell. 95, 103797 (2020)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"12","key":"2258_CR31","doi-asserted-by":"publisher","first-page":"5369","DOI":"10.1109\/TSMC.2018.2876455","volume":"50","author":"F Tan","year":"2020","unstructured":"F. Tan, X. Guan, Research progress on intelligent system \u2019s learning, optimization, and control\u2014part II: online sparse kernel adaptive algorithm. IEEE Trans. Syst. Man Cybern. Syst. 50(12), 5369\u20135385 (2020)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"4747","key":"2258_CR32","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1126\/science.232.4747.243","volume":"232","author":"GK Vallis","year":"1986","unstructured":"G.K. Vallis, El ni\u00f1o: A chaotic dynamical system? Science 232(4747), 243\u2013245 (1986)","journal-title":"Science"},{"issue":"10","key":"2258_CR33","first-page":"3371","volume":"68","author":"H Wang","year":"2021","unstructured":"H. Wang, X. Li, D. Bi, X. Xie, Y. Xie, A robust student\u2019s t-based kernel adaptive filter. IEEE Trans. Circuits Syst. II Express Briefs 68(10), 3371\u20133375 (2021)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"10","key":"2258_CR34","doi-asserted-by":"publisher","first-page":"4097","DOI":"10.1007\/s00034-017-0502-0","volume":"36","author":"W Wang","year":"2017","unstructured":"W. Wang, H. Zhao, B. Chen, Robust adaptive volterra filter under maximum correntropy criteria in impulsive environments. Circuits Syst. Signal Process. 36(10), 4097\u20134117 (2017)","journal-title":"Circuits Syst. Signal Process."},{"key":"2258_CR35","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.sigpro.2015.04.024","volume":"117","author":"Z Wu","year":"2015","unstructured":"Z. Wu, J. Shi, X. Zhang, W. Ma, B. Chen, I. Senior Member, Kernel recursive maximum correntropy. Signal Process. 117, 11\u201316 (2015)","journal-title":"Signal Process."},{"issue":"11","key":"2258_CR36","doi-asserted-by":"publisher","first-page":"5497","DOI":"10.1109\/TCYB.2019.2959834","volume":"51","author":"K Xiong","year":"2020","unstructured":"K. Xiong, H.H. Iu, S. Wang, Kernel correntropy conjugate gradient algorithms based on half-quadratic optimization. IEEE Trans. Cybern. 51(11), 5497\u20135510 (2020)","journal-title":"IEEE Trans. Cybern."},{"issue":"16","key":"2258_CR37","doi-asserted-by":"publisher","first-page":"4377","DOI":"10.1109\/TSP.2018.2853109","volume":"66","author":"M Zhang","year":"2018","unstructured":"M. Zhang, X. Wang, X. Chen, A. Zhang, The kernel conjugate gradient algorithms. IEEE Trans. Signal Process. 66(16), 4377\u20134387 (2018)","journal-title":"IEEE Trans. Signal Process."},{"issue":"9","key":"2258_CR38","doi-asserted-by":"publisher","first-page":"4346","DOI":"10.1007\/s00034-021-01691-z","volume":"40","author":"C Zhao","year":"2021","unstructured":"C. Zhao, W. Ren, M. Han, Adaptive sparse quantization kernel least mean square algorithm for online prediction of chaotic time series. Circuits Syst. Signal Process. 40(9), 4346\u20134369 (2021)","journal-title":"Circuits Syst. Signal Process."},{"issue":"12","key":"2258_CR39","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1109\/LSP.2017.2761886","volume":"24","author":"J Zhao","year":"2017","unstructured":"J. Zhao, H. Zhang, Kernel recursive generalized maximum correntropy. IEEE Signal Process. Lett. 24(12), 1832\u20131836 (2017)","journal-title":"IEEE Signal Process. Lett."},{"issue":"7","key":"2258_CR40","first-page":"963","volume":"65","author":"J Zhao","year":"2018","unstructured":"J. Zhao, H. Zhang, G. Wang, Projected kernel recursive maximum correntropy. IEEE Trans. Circuits Syst. II Express Briefs 65(7), 963\u2013967 (2018)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"2258_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103547","volume":"91","author":"K Zhong","year":"2020","unstructured":"K. Zhong, J. Ma, M. Han, Online prediction of noisy time series: dynamic adaptive sparse kernel recursive least squares from sparse and adaptive tracking perspective. Eng. Appl. Artif. Intell. 91, 103547 (2020)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"2258_CR42","unstructured":"G. Zoutendijk, Nonlinear programming, computational methods, in Integer & Nonlinear Programming (1970), pp. 37\u201386"}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-022-02258-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-022-02258-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-022-02258-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T04:49:15Z","timestamp":1681534155000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-022-02258-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":42,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["2258"],"URL":"https:\/\/doi.org\/10.1007\/s00034-022-02258-2","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"type":"print","value":"0278-081X"},{"type":"electronic","value":"1531-5878"}],"subject":[],"published":{"date-parts":[[2022,12,29]]},"assertion":[{"value":"12 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}]}}