The hesitant fuzzy BiRNN based on twice-cycle mechanism and its intelligent applications

Abstract

Existing intelligent classification methods could be inefficient to deal with the hybrid environments including hesitant fuzzy information and real numbers. With respect to this real issue, in this study, we propose some new intelligent methods to achieve deep learning and intelligent classification under this hybrid environment. To do this, we construct the hesitant fusion bidirectional recurrent neural network (HF-BiRNN) based on the hesitant fusion mechanism. Then, the twice-cycle mechanism is designed, which includes the extension mechanism and the decomposition-reorganization mechanism, to fully utilize the original data and optimize the classification results. Meanwhile, the overlap degree algorithm is constructed to filter the optimal outputs. After that, we further propose the hesitant expansion BiRNN (HE-BiRNN) by combining with the twice-cycle mechanism, overlap degree algorithm, and BiRNN. Lastly, these new methods are used to the problems of driving route classification and red wine quality assessment. The derived optimal results and comparison analysis fully show the effectiveness and feasibility of the new proposed mechanisms and developed models.