Neural networks (NNs), type-1 fuzzy logic systems (T1FLSs), and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to\r\nbe universal approximators,whichmeans that they can approximate any nonlinear continuous function. Recent research shows that\r\nembedding an IT2FLS on an NN can be very effective for a wide number of nonlinear complex systems, especially when handling\r\nimperfect or incomplete information. In this paper we show, based on the Stone-Weierstrass theorem, that an interval type-2 fuzzy\r\nneural network (IT2FNN) is a universal approximator,which uses a set of rules and interval type-2membership functions (IT2MFs)\r\nfor this purpose. Simulation results of nonlinear function identification using the IT2FNN for one and three variables and for the\r\nMackey-Glass chaotic time series prediction are presented to illustrate the concept of universal approximation.
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