Convolutional Fuzzy Neural Networks With Random Weights for Image Classification

Deep fuzzy neural networks have established a fundamental connection between fuzzy systems and deep learning networks, serving as a crucial bridge between two research fields in computational intelligence. These hybrid networks have powerful learning capability stemming from deep neural networks while leveraging the advantages of fuzzy systems, such as robustness. Due to these benefits, deep fuzzy neural networks have recently been an emerging topic in computational intelligence. With the help of deep learning, fuzzy systems have achieved great performance on the classification task. Although fuzzy systems have been extensively investigated, they still struggle in terms of image classification. In this paper, we propose a convolutional fuzzy neural network that combines improved convolutional neural networks with a fuzzy-set-based fusion technique. Different from convolutional neural networks, filters are randomly generated in convolutional layers in our model. This operation not only leads to the fast learning of the model but also avoids some notorious problems of gradient descent procedures in conventional deep learning methods. Extensive experiments demonstrate that the proposed approach is competitive with state-of-the-art fuzzy models and deep learning models. Compared to classical deep models that require massive training data, the proposed approach works well on small datasets.