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

Abstract

Interval type-2 fuzzy neural networks &#x0028 IT2FNNs &#x0029 can be seen as the hybridization of interval type-2 fuzzy systems &#x0028 IT2FSs &#x0029 and neural networks &#x0028 NNs &#x0029. 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 &#x0028 BBBC &#x0029 optimization and particle swarm optimization &#x0028 PSO &#x0029 are applied in the parameter optimization for Takagi-Sugeno-Kang &#x0028 TSK &#x0029 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 &#x0028 IT2FMFs &#x0029 and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2FNNs.