An interval type-2 T-S fuzzy classification system based on PSO and SVM for gender recognition

Abstract

In this paper, an interval type-2 Takagi-Sugeno fuzzy classification system (IT2T-SFCS) learned by particle swarm optimization (PSO) and support vector machine (SVM) for antecedent and consequent parameters optimization is proposed. The IT2T-SFCS is constructed by fuzzy if-then rules whose antecedents are interval type-2 fuzzy sets and consequents are linear state equations. The antecedents of IT2T-SFCS use the fuzzy iterative self-organizing data analysis technique (ISODATA) and PSO to learn and calculate the optimal centers and the uncertain widths of the Gaussian membership functions. Consequent parameters in IT2T-SFCS are learned through SVM for the purpose of achieving higher generalization ability. The proposed IT2T-SFCS is able to directly handle uncertainties, minimize the effects of uncertainties and get the better generalization performance, which inherits the benefits of interval type-2 T-S fuzzy system and SVM. For demonstration, IT2T-SFCS is used as a classifier in gender recognition. The experimental results show that the performance of the proposed IT2T-SFCS is superior to that of the previous mainstream classifiers.

A Heuristic T-S Fuzzy Model for the Pumped-Storage Generator-Motor Using Variable-Length Tree-Seed Algorithm-Based Competitive Agglomeration

“…Moreover, another drawback is that a certain predefined number of clusters is needed a priori. In order to obtain the global optimal partition of the fuzzy space and avoid premature solutions, swarm intelligence optimizers with stronger capability of global searching and escaping from local optimum, such as genetic algorithm (GA) [25], particle swarm optimization (PSO) [26], artificial bee colony(ABC) [27] and differential evolution (DE) [28,29], have been applied to the fuzzy partition of T-S fuzzy modeling in recent years. Thirdly, least square (LS) method has been utilized in consequence parameters identification of T-S fuzzy model for its excellent parameter estimation capability and computational efficiency.…”