|Forecasting by General Type-2 Fuzzy Logic Systems Optimized with QPSO Algorithms
Yang Chen* and Dazhi Wang
International Journal of Control, Automation, and Systems, vol. 15, no. 6, pp.2950-2958, 2017
Abstract : "With the development of a-planes representation result of general type-2 fuzzy sets, the optimization
and application of general type-2 fuzzy logic systems (GT2 FLSs) based on general type-2 fuzzy sets (GT2 FSs)
has become a hot topic in current academic research. The efficient and energy conserving permanent magnetic
drive (PMD) presents relatively high uncertainty as an emerging technology. The paper studies on forecasting problems
based the data of torque and revolutions per minute (rpm) of PMD. In the proposed GT2 FLSs design, the
antecedent, input measurement primary membership functions of GT2 FSs are chosen as Gaussian type-2 membership
functions with uncertain standard deviation. While the consequent parameters are selected as deterministic
values. Quantum particle swarm optimization (QPSO) algorithms are used to optimize all the parameters of the
suggested GT2 FLSs. The torque and rpm data of PMD are used to train and test the proposed advanced FLSs forecasting
methods. Simulation studies and convergence analysis show the effectiveness of the proposed GT2 FLSs
methods compared with their type-1 (T1) and interval type-2 (IT2) methods for forecasting."
"General type-2 fuzzy logic systems, general type-2 fuzzy sets, -planes, quantum particle swarm optimization algorithms, forecasting, simulation."
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