桥梁技术:深度学习和模糊逻辑应用综述

Dinah Mohammed, Raidah S. Khudeye
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摘要

摘要--建模和预测领域拥有各种实际应用,例如深度学习,它是该领域使用的一种强大工具。事实证明,深度学习是从复杂数据源中提取极其准确预测结果的重要技术。递归神经网络在语言翻译和字幕制作方面也证明了其实用性。然而,卷积神经网络仍然是图像分类任务的主流解决方案。此外,深度学习(又称深度神经网络)涉及训练具有多层相互连接的人工神经元的模型。深度学习的主要理念是通过提升抽象层次来学习数据表示。这些策略很有效,但无法解释结果是如何产生的。在不知道如何使用深度学习得出解决方案的情况下。在人工智能领域,深度学习和模糊逻辑是两种强大的技术。此外,模糊逻辑还与深度学习相结合,帮助深度学习选择所需的特征,并在没有监督的情况下工作,从而使开发具有丰富 DL 信息的可靠系统成为可能,即使没有手工标记的数据。解释这些特征的模糊逻辑随后将解释系统对分类标签的选择。 本调查将重点介绍利用模糊逻辑改进深度学习的各种应用。
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Bridging Techniques: A Review of Deep Learning and Fuzzy Logic Applications
Abstract—The modelling and prediction field boasts various practical applications, such as deep learning, which is a powerful tool used in this field. It has been proved that deep learning is a valuable technique for extracting extremely accurate predictions from complex data sources. Recursive neural networks have also demonstrated usefulness in language translation and caption production. However, 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 rising levels of abstraction. These strategies are effective but do not 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 to help deep learning select the desired features and work without supervision, making it possible to develop reliable systems with rich DL information even without hand-labelled data. Fuzzy logic that interprets these features will subsequently explain the system's choice of classification label.  This survey highlights the various applications which use fuzzy logic to improve deep learning. .
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