Review: Deep Learning and Fuzzy Logic Applications

Abstract

The modeling and prediction field bosses a variety of practical applications, deep learning is a powerful tool used in this field. It has been proved that deep learning is a useful technique for extracting extremely accurate predictions from complex data sources, and also Recursive neural networks have demonstrated their usefulness in language translation and caption production, but convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers of interconnected artificial neurons. The primary idea of deep learning is to learn data representations through raising levels of abstraction. These strategies are effective, but they don't explain how the result is produced. Without knowing how a solution is arrived at using deep learning. In the field of artificial intelligence, deep learning and fuzzy logic are two powerful techniques. In addition, fuzzy logic combines deep learning will help deep learning select the desired features and work without supervision, this will make it possible to develop reliable systems with rich DL information even in the absence of hand-labeled data. Fuzzy logic that interpreted these features will subsequently provide explanations for the system's choice of classification label. This survey highlights the various applications which use fuzzy logic to improve deep learning.