{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T03:06:32Z","timestamp":1772679992928,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2016,12,6]],"date-time":"2016-12-06T00:00:00Z","timestamp":1480982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["2010-0020163"],"award-info":[{"award-number":["2010-0020163"]}]},{"name":"MSIP(Ministry of Science, ICT and Future Planning), Korea","award":["IITP-2016-H8601-16-1003"],"award-info":[{"award-number":["IITP-2016-H8601-16-1003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly\u2014even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA.<\/jats:p>","DOI":"10.3390\/sym8120148","type":"journal-article","created":{"date-parts":[[2016,12,6]],"date-time":"2016-12-06T10:07:17Z","timestamp":1481018837000},"page":"148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["EMG Pattern Classification by Split and Merge Deep Belief Network"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4337-3264","authenticated-orcid":false,"given":"Hyeon-min","family":"Shim","sequence":"first","affiliation":[{"name":"Department of Digital Electronics, Dong Seoul University, Seongnam 13117, Korea"}]},{"given":"Hongsub","family":"An","sequence":"additional","affiliation":[{"name":"Korea Electrotechnology Research Institute, Ansan 15588, Korea"}]},{"given":"Sanghyuk","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"},{"name":"Biomedical Engineering Centre, Chiang Mai University, Chiang Mai 50200, Thailand"}]},{"given":"Eung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Korea Polytechinic University, Siheung 15073, Korea"}]},{"given":"Hong-ki","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea"}]},{"given":"Sangmin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Inha University, Incheon 22201, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2016,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/10.204774","article-title":"A new strategy for multifunction myoelectric control","volume":"40","author":"Hudgins","year":"1993","journal-title":"IEEE Trans. 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