Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods

Interval type-2 fuzzy neural networks IT2FNNs can be seen as the hybridization of interval type-2 fuzzy systems IT2FSs and neural networks NNs . Thus, they naturally inherit the merits of both IT2FSs and NNs. Although IT2FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2FNNs, which increases the difficulties of their design. In this paper, big bang-big crunch BBBC optimization and particle swarm optimization PSO are applied in the parameter optimization for Takagi-Sugeno-Kang TSK type IT2FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions IT2FMFs and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2FNNs.