Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many different real-world applications. Artificial neural networks and fuzzy logic are two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the hybrid models, the fuzzy min-max (FMM) neural network has been proven to be a premier model for undertaking pattern classification problems. While FMM is useful in terms of its capability of online learning, it suffers from several limitations in the learning procedure. Therefore, over the past years, researchers have proposed numerous improvements to overcome the limitations of the original FMM model. This paper carries out a comprehensive survey of the developments conducted on the FMM model for pattern classification. In order to assist recent researchers in selecting the most suitable FMM variant and to provide proper guidance for future developments, this study divides the variants of FMM into two main board categories, namely FMM variants with and without contraction. This division facilitates understanding of the developments conducted by researchers on the original FMM neural network, as well as provides the scope to identify the limitations that still exist in the FMM models. This paper also summarizes the use of FMM and its variants in solving different benchmark and real-world problems. Finally, the possible future trends are highlighted.

中文翻译：

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模式分类变体模糊 Min__ax 神经网络及其应用综述


在过去的几十年里，模式分类已成为人工智能最重要的领域之一，因为它构成了许多不同的现实应用中的重要组成部分。人工神经网络和模糊逻辑是模式分类中使用最广泛的两种模型。为了构建高效且强大的模型，研究人员引入了结合模糊逻辑和人工神经网络的混合模型。在混合模型中，模糊最小最大（FMM）神经网络已被证明是解决模式分类问题的首要模型。虽然 FMM 在在线学习能力方面很有用，但它在学习过程中存在一些限制。因此，在过去的几年里，研究人员提出了许多改进来克服原始FMM模型的局限性。本文对用于模式分类的 FMM 模型的发展进行了全面的综述。为了帮助近期研究人员选择最合适的FMM变体并为未来的发展提供适当的指导，本研究将FMM的变体分为两大类，即带收缩的FMM变体和不带收缩的FMM变体。这种划分有助于理解研究人员在原始 FMM 神经网络上进行的开发，并提供了识别 FMM 模型中仍然存在的局限性的范围。本文还总结了 FMM 及其变体在解决不同基准和现实问题中的使用。最后，强调了未来可能的趋势。