<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Slimane, Noureddine</style></author><author><style face="normal" font="default" size="100%">Chafaa, Kheireddine</style></author><author><style face="normal" font="default" size="100%">Mohamed Salah Khireddine</style></author><author><style face="normal" font="default" size="100%">Ghanai, Mouna</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy estimation for an adaptive type-2 fuzzy logic controller</style></title><secondary-title><style face="normal" font="default" size="100%">NNGT Int. J. on Artificial Intelligence, </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.680.5241&amp;rep=rep1&amp;type=pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	It has been proven that fuzzy systems called type-1 fuzzy systems can approximate any nonlinear function to any desired accuracy because of the universal approximation theorem. The principal problem encountered with type-1 fuzzy systems is that they can deliver a non satisfactory performance in face of uncertainty and imprecision. In this paper, a new type-2 fuzzy system based on type-2 fuzzy basis functions was developed in order to use them in an indirect adaptive control.
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