This survey reviews recent researches conducted on the application of fuzzy approaches to Case-Based Reasoning (CBR) dealing with real-time applications. Fuzzy approaches have been effectively applied for knowledge representation, feature selection, and learning in CBR. Dealing with imprecise and uncertain knowledge, generalization, mining, and learning also in combination with low computational complexity are the main advantages of fuzzy approaches used in the CBR context. This paper presents and summarizes new findings on the integration of fuzzy approaches with CBR. The survey results highlight the advantages of fuzzy approaches in CBR for real-time applications. They show the current state of fuzzy-based CBR approaches. In addition, fuzzy approaches which are more operative for each operation in CBR are addressed. Those operations most contributing to the advantages of the fuzzy approach will be pointed out and detailed. Low accuracy, storage and computational challenges with a large amount of experiences and uncertainties are important issues in case of real-time applications. This paper proposes a general fuzzy-based CBR approach for real-time applications to benefit the advantages of previous approaches. Finally, some considerations of latest developments in fuzzy approaches which may be introduced as potential research directions for real-time applications are stated.

中文翻译：

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动态环境中实时应用的基于案例推理和模糊方法的集成：现状和未来方向

本次调查回顾了最近将模糊方法应用于基于案例推理 (CBR) 处理实时应用程序的研究。模糊方法已有效地应用于 CBR 中的知识表示、特征选择和学习。处理不精确和不确定的知识、泛化、挖掘和学习以及低计算复杂度是 CBR 上下文中使用的模糊方法的主要优点。本文介绍并总结了模糊方法与 CBR 集成的新发现。调查结果突出了模糊方法在 CBR 中实时应用的优势。它们显示了基于模糊的 CBR 方法的当前状态。此外，还讨论了对 CBR 中的每个操作更有效的模糊方法。将指出并详细说明最有助于模糊方法优势的那些操作。在实时应用的情况下，具有大量经验和不确定性的低准确度、存储和计算挑战是重要的问题。本文提出了一种用于实时应用的基于模糊的通用 CBR 方法，以利用以前方法的优点。最后，阐述了对模糊方法的最新发展的一些考虑，这些发展可能被引入作为实时应用的潜在研究方向。本文提出了一种用于实时应用的基于模糊的通用 CBR 方法，以利用以前方法的优点。最后，阐述了对模糊方法的最新发展的一些考虑，这些发展可能被引入作为实时应用的潜在研究方向。本文提出了一种用于实时应用的基于模糊的通用 CBR 方法，以利用以前方法的优点。最后，阐述了对模糊方法的最新发展的一些考虑，这些发展可能被引入作为实时应用的潜在研究方向。