A Survey on Fuzzy Deep Neural Networks

Abstract

Deep neural networks are a class of powerful machine learning model that uses successive layers of non-linear processing units to extract features from data. However, the training process of such networks is quite computationally intensive and uses commonly used optimization methods that do not guarantee optimum performance. Furthermore, deep learning methods are often sensitive to noise in data and do not operate well in areas where data are incomplete. An alternative, yet little explored, method in enhancing deep learning performance is the use of fuzzy systems. Fuzzy systems have been previously used in conjunction with neural networks. This survey explores the different ways in which deep learning is improved with fuzzy logic systems. The techniques are classified based on how the two paradigms are combined. Finally, the real-life applications of the models are also explored.