A type-2 fuzzy neural model based control of a nonlinear system

We propose a novel method for controlling a nonlinear system using the neuro-fuzzy model based on the type-2 fuzzy sets. The type-2 fuzzy logic controller (FLC) based neuro-fuzzy model handle the rule uncertainties. Lot of work has been done in the field of control using the type-1 FLC and NN or their combination where different models could minimize the error with greater efficiency (Poggio and Girosi, 1990; Adetona and Keel, 2000; Azeem et al., 2000; Narendra and Parathasarathy, 1990; Lin and Cunningham, 1995). But in the practical case they are unable to handle uncertainties in the rules, which lead to inaccuracy in the actual output. This can be very efficiently handled by the type-2 FLC (Karnik et al., 1999; Liang and Mendel, 2000). As in the control purpose the handling of uncertainties is a necessary and an important issue. This issue is more important as it has to handle the uncertainties of a plant (as the control action taken by the controller effect the plant), so we need a powerful tool, which fulfil our need. The type-2 based controller is a necessary and sufficient answer of the above problem. The implementation of type-2 FLC involves the operation of fuzzification, inference and output processing. Fuzzification and inference process include; finding the number of rules, i.e., rule base formulation and learning the parameters of the membership function. We focus on the output processing, i.e., defuzzification, which is done by the neural network. Here we show how type-2 FLC can handle uncertainties in the rules with a good approximation, which can't be handled by the type-1 FLC