基于深度学习的直觉模糊集隶属度确定方法

直觉模糊集隶属度、非隶属度和犹豫度的确定方法是直觉模糊集理论与应用研究中一个十分重要的问题,其直接影响着相关方法的可扩展性及应用结果。然而,现有方法存在主观性强、标准难以统一等问题,并且大多基于模拟数据进行实验,难以应用至实际数据。针对上述问题以及大规模非结构化数据,提出一种基于深度学习的直觉模糊集隶属度、非隶属度和犹豫度确定方法。新方法克服了传统方法的技术和思维局限,拓展了直觉模糊集相关问题的研究思路,为其实际应用提供了更多可能。

Deep Learning-Based Determination Method for Membership Degree in Intuitionistic Fuzzy Sets

The determination of the membership,non-membership,and hesitation degrees is an important issue in intuitionistic fuzzy sets(IFS)research since it can directly influence the extendibility and application of relevant researches.However,there are many limitations in existing methods for determining the membership degree in IFS,such as subjectivity and inconsistency of standards.Most experiments are based on simulated data and are unsuitable for practical data.Aimed at the above problems and large-scale unstructured data,a method for determining membership,non-membership and hesitation in IFS based on deep learning is proposed.In this work,a Convolutional Neural Network and Long Short-Term Memory(CNN-LSTM)model is constructed based on text,and it can automatically calculate the membership,non-membership and hesitation degrees of the text after the training process.Besides,the new method can be applied to other fields by modeling and training.The proposed method overcomes the technical and thinking limitations of traditional methods,expands the research ideas of IFS,and provides possibilities for its practical application.