Application of Fuzzy Deep Learning to Classify Images

The strengths of fuzzy logic and deep learning are combined in this paper to propose a hierarchical fused fuzzy deep neural network model for data classification. By combining linguistic expertise from fuzzy logic with learnt representations from deep neural networks, the suggested approach seeks to improve classification accuracy and interpretability. Input, fuzzy logic, feature extraction, fusion, and decision layers are just a few of the many layers that make up the model. Through linguistic variables, membership functions, and fuzzy rules, the fuzzy logic layer captures uncertainty and imprecision in the incoming data. In order to extract high-level representations from the data, the feature extraction layer makes use of deep neural networks. The learnt representations from the feature extraction layer are combined with the fuzzy inference outcomes from the fuzzy logic layer in the fusion layer. The combination of language and learnt elements is made possible by this fusion process, which results in a better representation of the input material. The final categorization is subsequently made by the decision layer using the combined data. The efficiency of the suggested model is demonstrated by tests performed on the CIFAR-10 dataset. Comparing the hierarchical fused fuzzy deep neural network to conventional methods and individual techniques, it provides enhanced classification accuracy. The model is capable of handling challenging and realistic classification tasks thanks to the integration of fuzzy logic with deep learning. The suggested model's mix of fuzzy logic with deep learning demonstrates excellent potential for tackling difficult issues in a variety of fields. The combined method improves accuracy, resilience, and interpretability, opening up new opportunities for the development of both artificial intelligence and data classification as a whole.