A Mamdani-type Recurrent Interval Type-2 Fuzzy Neural Network for Identification of Dynamic Systems with Measurement Noise
نویسنده
چکیده
Recurrent fuzzy neural networks (FNNs) have been widely applied to dynamic system processing problems. However, most recurrent FNNs focus on the use of type-1 fuzzy sets. This paper proposes a Mamdani-type recurrent interval type-2 FNN (M-RIT2FNN) that uses interval type-2 fuzzy sets in both rule antecedent and consequent parts. The reason for using interval type-2 fuzzy sets is to increase network noise resistance. The M-RIT2FNN uses self-feedback loops for memorizing past states and past control inputs of an identified plant. For identification problems, it is unnecessary to know the plant order or input delay number in advance when using the M-RIT2FNN. The M-RIT2FNN identifies a plant via online structure and parameter learning. Simulation results on noisy plant identification and comparisons with different recurrent FNNs verify the advantage of the M-RIT2FNN.
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