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This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of brain signals between normal and abnormal. For this goal, different types of optimization algorithms for the learning of an ANFIS system are evaluated, such as the backpropagation, the mini-lots, and the Adam algorithm (adaptive moment estimation). As a result, utilizing the ANFIS with Adam and mini-lots provides the most accurate, fastest, and with least computational costs results.<\/jats:p>","DOI":"10.3233\/jifs-190207","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T11:51:12Z","timestamp":1561117872000},"page":"4033-4041","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":28,"title":["ANFIS system for classification of brain signals"],"prefix":"10.1177","volume":"37","author":[{"given":"Jos\u00e9 de Jes\u00fas","family":"Rubio","sequence":"first","affiliation":[{"name":"Secci\u00f3n de Estudios de Posgrado e Investigaci\u00f3n, ESIME Azcapotzalco, Instituto Polit\u00e9cnico Nacional, Av. de las Granjas no. 682, Col. 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