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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 …