Evolutionary hierarchical fuzzy modeling of Interval Type-2 Beta Fuzzy Systems
Yosra Jarraya, Souhir Bouaziz, Adel M. Alimi, Ajith Abraham · 2016
The automated evolutionary design of an optimal hierarchical fuzzy system combined with the use of Interval Type-2 Fuzzy Systems and the Beta basis function is considered in this study. The resulted proposed system is named the Hierarchical interval Type-2 Beta Fuzzy System (HT2BFS). For the learning process, two main optimizations steps are considered. The first one executes the structure learning of the HT2BFS by the Extended Genetic Programming (EGP) algorithm allowing the generation of an optimal architecture. In the second step, the Opposite-based Particle Swarm Optimization (OPSO) algorithm is employed for the adjustment of parameters existing in the best obtained architecture. The two optimization algorithms are interleaved until an optimal HT2BFS is generated. Experiments on some time-series forecasting problems were performed and prove the effectiveness of the proposed system.