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This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead\u2010zone function is not required anymore. On the basis of this modification and by using a Lyapunov\u2010like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.<\/jats:p>","DOI":"10.1155\/2012\/529176","type":"journal-article","created":{"date-parts":[[2012,4,12]],"date-time":"2012-04-12T21:06:13Z","timestamp":1334264773000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["System Identification Using Multilayer Differential Neural Networks: A New Result"],"prefix":"10.1155","volume":"2012","author":[{"given":"J. Humberto","family":"P\u00e9rez-Cruz","sequence":"first","affiliation":[]},{"given":"A. 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