A new indirect approach to the type-2 fuzzy systems modeling and design

PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights

A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. A dynamic adjustment of the PSO allows the algorithm to behave better in the search for optimal results because the dynamic adjustment provides good synchrony between the exploration and exploitation of the algorithm. Results of experiments and a comparison between traditional neural networks and the fuzzy neural networks with interval type-2 fuzzy numbers weights using T-norms and S-norms are given to prove the performance of the proposed approach. For testing the performance of the proposed approach, some cases of time series prediction are applied, including the stock exchanges of Germany, Mexican, Dow-Jones, London, Nasdaq, Shanghai, and Taiwan.

Alpha-plane based automatic general type-2 fuzzy clustering based on simulated annealing meta-heuristic algorithm for analyzing gene expression data

This paper considers microarray gene expression data clustering using a novel two stage meta-heuristic algorithm based on the concept of α-planes in general type-2 fuzzy sets. The main aim of this research is to present a powerful data clustering approach capable of dealing with highly uncertain environments. In this regard, first, a new objective function using α-planes for general type-2 fuzzy c-means clustering algorithm is represented. Then, based on the philosophy of the meta-heuristic optimization framework 'Simulated Annealing', a two stage optimization algorithm is proposed. The first stage of the proposed approach is devoted to the annealing process accompanied by its proposed perturbation mechanisms. After termination of the first stage, its output is inserted to the second stage where it is checked with other possible local optima through a heuristic algorithm. The output of this stage is then re-entered to the first stage until no better solution is obtained. The proposed approach has been evaluated using several synthesized datasets and three microarray gene expression datasets. Extensive experiments demonstrate the capabilities of the proposed approach compared with some of the state-of-the-art techniques in the literature.