Fuzzy-rough cognitive networks (FRCNs) are interpretable recurrent neural networks, primarily designed for solving classification problems. Their structure is simple and transparent, while the performance is comparable to the well-known black-box classifiers. Although there are many applications on fuzzy cognitive maps and recently for FRCNS, only a very limited number of studies discuss the theoretical issues of these models. In this paper, we examine the behaviour of FRCNs viewing them as discrete dynamical systems. It will be shown that their mathematical properties highly depend on the size of the network, i.e., there are structural differences between the long-term behaviour of FRCN models of different size, which may influence the performance of these modelling tools.

中文翻译：

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模糊粗糙认知网络的动力学

模糊粗糙认知网络（FRCN）是可解释的递归神经网络，主要用于解决分类问题。它们的结构简单透明，而性能可与著名的黑匣子分类器相媲美。尽管在模糊认知图上有许多应用，最近在FRCNS上也有应用，但是只有极少数的研究讨论了这些模型的理论问题。在本文中，我们检查了FRCN的行为，将其视为离散的动力学系统。将显示它们的数学特性高度依赖于网络的大小，即不同大小的FRCN模型的长期行为之间存在结构差异，这可能会影响这些建模工具的性能。