|Design of type-2 Fuzzy Logic Systems Based on Improved Ant Colony Optimization
Zhifeng Zhang, Tao Wang*, Yang Chen, and Jie Lan
International Journal of Control, Automation, and Systems, vol. 17, no. 2, pp.536-544, 2019
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."
"Ant colony optimization, A2-C1 type fuzzy logic system, improved ant colony optimization, neural network."