Training Tsukamoto-Type Neural Fuzzy Inference Network Based on Cat Swarm Optimization

This paper introduces a new approach for training the Tsukamoto-Type neural fuzzy inference network (TNFIN). In the standard method, the antecedent and consequent parameters are trained by a hybrid learning algorithm combining the Least Square Estimation (LSE) method and the Gradient Descent (GD) method. In this study in order to tune the parameters of TNFIN, a new swarm-based optimization algorithm is applied. Cat Swarm Optimization as a novel swarm intelligence algorithm which is used for global optimization problems is used to train nonlinear parameters of TNFIN. Experimental result for prediction of Mackey-Glass model and identification of a nonlinear dynamic system indicates that the performance of proposed algorithm in comparison with standard method is much better and it shows quite satisfactory results.

Keywords:

Cat Swarm Optimization , Tsukamoto Fuzzy Model , Prediction and Identification