In this study, we propose a novel fast learning data-driven method for the design of interval type-2 fuzzy logic system (IT2FLS). In order to accelerate the learning speed, we present a parallel mechanism to generate the antecedents of the IT2FLS and the least square method based learning algorithm to optimize the consequents. Firstly, driven by different sub-data sets, corresponding type-1 fuzzy logic systems (T1FLSs) which have the same initial fuzzy partition (thus the same initial fuzzy rule base) are parallelly obtained through the popular ANFIS method. Then, an ensembling strategy is proposed to form the type-2 fuzzy partition for each input variable through merging corresponding type-1 fuzzy sets (T1FSs) in the type-1 fuzzy partitions of the learned T1FLSs. By this strategy, the antecedents of the IT2FLS are determined and then fixed, however, the consequent parameters still need to be optimized. To achieve both excellent performance and fast training speed, a least square method based learning algorithm is provided for the optimization of the consequent parameters. Finally, three benchmark problems and one real-world application are given, and detailed comparisons with some well performed methods are made. Simulation and comparison results have verified the effectiveness and superiorities of the proposed method.
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