Title :
Type-2 GA-TSK fuzzy neural network
Author :
Cai, Alvin ; Quek, Chai ; Maskell, Douglas L.
Author_Institution :
Nanyang Technol. Univ., Singapore
Abstract :
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.
Keywords :
fuzzy logic; fuzzy neural nets; genetic algorithms; Takagi-Sugeno-Kang fuzzy neural network; genetic algorithm; neuro-fuzzy classifiers; rule uncertainties; type-2 GA-TSK fuzzy neural network; type-2 fuzzy logic system; Evolutionary computation; Fuzzy neural networks;
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
DOI :
10.1109/CEC.2007.4424661