Design of type-2 Fuzzy Logic Systems Based on Improved Ant Colony Optimization

Abstract

An Improved Ant Colony Optimization (IACO) is proposed to design A2-C1 type fuzzy logic system (FLS) in the paper. The design includes parameters adjustment and rules selection, and the performance of the intelligent fuzzy system, which can be improved by choosing the most optimal parameters and reducing the redundant rules. In order to verify the feasibility of the proposed algorithm, the intelligence fuzzy logic systems based on the algorithms are applied to predict the Mackey-Glass chaos time series. The simulations show that both the IACO and ACO have better tracking performances. The results compared with classical algorithm BP ( back-propagation design) shows the tracking performance of IACO is more precise, the result compared with ACO shows that either the training result or the testing result, the tracking performance of IACO is better, and IACO has a faster convergence rate than ACO, the results compared with the Intelligent type-1 fuzzy logic systems show that both the A2-C1 type FLS based on IACO and ACO have better tracking performance than type-1 fuzzy logic system.