Type-2 GA-TSK fuzzy neural network

A novel fuzzy-neural network, the type-2 GA- TSKfnn (T2GA-TSKfnn), combining a type-2 fuzzy logic system (FLS) and a genetic algorithm (GA) based Takagi-Sugeno- Kang fuzzy neural network (GA-TSKfnn), is presented. The rational for this combination is that type-2 fuzzy sets are better able to deal with rule uncertainties, while the optimal GA-based tuning of the T2GA-TSKfnn parameters achieves better classification results. However, a general T2GA-TSKfnn is computationally very intensive due to the complexity of the type-2 to type-1 reduction. Therefore, we adopt an interval T2GA-TSKfnn implementation to simplify the computational process. Simulation results are provided to compare the T2GA-TSKfnn against other fuzzy neural networks. These results show that the proposed system is able to achieve a higher classification rate when compared against a number of other traditional neuro-fuzzy classifiers.