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The fusion neural system combines the features that are extracted from two-independent neural streams, a feedforward and a diagonal recurrent one, satisfying different design criteria of the identification task. Simulation results for five cases reveal the approximation strength of both proposed fusion neural architecture and proposed learning algorithm. Also, additional experiments demonstrate the effectiveness in cases of parameter variations and additive noise. <\/jats:p>","DOI":"10.1142\/s0129065719500151","type":"journal-article","created":{"date-parts":[[2019,4,22]],"date-time":"2019-04-22T02:05:46Z","timestamp":1555898746000},"page":"1950015","source":"Crossref","is-referenced-by-count":12,"title":["Lyapunov Theory-Based Fusion Neural Networks for the Identification of Dynamic Nonlinear Systems"],"prefix":"10.1142","volume":"29","author":[{"given":"Spyridon","family":"Plakias","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiannis S.","family":"Boutalis","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2019,10,28]]},"reference":[{"key":"S0129065719500151BIB001","volume-title":"Applied System Identification","author":"Juang J.-N.","year":"1994"},{"key":"S0129065719500151BIB002","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-04323-3"},{"key":"S0129065719500151BIB003","doi-asserted-by":"publisher","DOI":"10.3182\/20080706-5-KR-1001.01215"},{"key":"S0129065719500151BIB004","doi-asserted-by":"publisher","DOI":"10.3166\/ejc.11.335-352"},{"key":"S0129065719500151BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2015.01.018"},{"key":"S0129065719500151BIB006","doi-asserted-by":"publisher","DOI":"10.3846\/13923730.2015.1068843"},{"key":"S0129065719500151BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2003.10.001"},{"key":"S0129065719500151BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(95)00120-8"},{"key":"S0129065719500151BIB009","doi-asserted-by":"publisher","DOI":"10.1080\/00207178908559683"},{"key":"S0129065719500151BIB010","volume-title":"Neural Networks: A Comprehensive Foundation","author":"Haykin S.","year":"1998","edition":"2"},{"key":"S0129065719500151BIB011","doi-asserted-by":"publisher","DOI":"10.1142\/9789812830388_0008"},{"key":"S0129065719500151BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/S1367-5788(03)00009-9"},{"key":"S0129065719500151BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2014.2345353"},{"key":"S0129065719500151BIB014","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065797000331"},{"key":"S0129065719500151BIB015","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065712500049"},{"key":"S0129065719500151BIB016","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065710002449"},{"key":"S0129065719500151BIB017","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065710002310"},{"key":"S0129065719500151BIB018","doi-asserted-by":"publisher","DOI":"10.1109\/72.80202"},{"key":"S0129065719500151BIB019","doi-asserted-by":"publisher","DOI":"10.1109\/60.103639"},{"key":"S0129065719500151BIB020","first-page":"359","volume-title":"IFAC Proc. 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