{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:22:21Z","timestamp":1769552541745,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,23]],"date-time":"2017-01-23T00:00:00Z","timestamp":1485129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance.<\/jats:p>","DOI":"10.3390\/e19010045","type":"journal-article","created":{"date-parts":[[2017,1,23]],"date-time":"2017-01-23T10:40:33Z","timestamp":1485168033000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2450-6028","authenticated-orcid":false,"given":"Yingsong","family":"Li","sequence":"first","affiliation":[{"name":"College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Yanyan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Engineering, Huazhong Agricultural University, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9850-6004","authenticated-orcid":false,"given":"Felix","family":"Albu","sequence":"additional","affiliation":[{"name":"Department of Electronics, Valahia University of Targoviste, Targoviste 130082, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gui, G., Kumagai, S., Mehbodniya, A., and Adachi, F. 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