Fuzzy Convolution Neural Network

Fuzzy Convolutional Neural Networks (FCNNs) integrate fuzzy logic with the architecture of Convolutional Neural Networks (CNNs) to improve the robustness and interpretability of deep learning models. Current research focuses on applying FCNNs to diverse data types, including tabular data, time series, and electroencephalographic (EEG) signals, often incorporating hybrid architectures like neuro-fuzzy inference systems and fuzzy temporal convolutional networks. This approach aims to enhance the accuracy and explainability of CNNs, particularly in handling noisy or uncertain data, leading to improved performance in applications ranging from data classification and forecasting to brain-computer interfaces.