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.

中文翻译：

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模糊深度神经网络综述

深度神经网络是一类强大的机器学习模型，它使用连续的非线性处理单元层从数据中提取特征。但是，此类网络的训练过程计算量很大，并且使用了不能保证最佳性能的常用优化方法。此外，深度学习方法通​​常对数据中的噪声很敏感，并且在数据不完整的区域不能很好地运行。增强深度学习性能的另一种但很少探索的方法是使用模糊系统。模糊系统以前已与神经网络结合使用。本调查探讨了使用模糊逻辑系统改进深度学习的不同方式。这些技术根据两种范式的组合方式进行分类。最后，