Rule-based fuzzy cognitive maps (RBFCMs) have been developed for modeling nonmonotonic, uncertain, cause-effect systems. However, the standard reasoning and impact accumulation mechanisms developed for RBFCMs assume that the level of variation that a fuzzy set represents is directly linked with the shape of the fuzzy set. It poses a big restriction on how the corresponding fuzzy sets have to be constructed. In this paper, we propose a new reasoning and impact accumulation mechanisms which take into consideration standard semantics of fuzzy sets, where their uncertainty is measured by fuzziness. New type of complex fuzzy relationships and reasoning on them is introduced to model a joint impact of several causal nodes on one effect node. With these new mechanisms, RBFCMs become much more flexible, provide more means to capture complexity of real-world systems, and are less computational demanding than standard mechanisms. The advantages of the new RBFCMs are demonstrated using different examples and compared with standard mechanisms.

中文翻译：

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基于规则的模糊认知地图推理和影响积累的新机制

基于规则的模糊认知图 (RBFCM) 已被开发用于对非单调、不确定、因果系统进行建模。然而，为 RBFCM 开发的标准推理和影响累积机制假设模糊集表示的变化水平与模糊集的形状直接相关。它对必须如何构造相应的模糊集提出了很大的限制。在本文中，我们提出了一种新的推理和影响累积机制，该机制考虑了模糊集的标准语义，其中它们的不确定性是通过模糊性来衡量的。引入了新型复杂模糊关系及其推理，以模拟多个因果节点对一个效应节点的联合影响。有了这些新机制，RBFCM 变得更加灵活，提供更多方法来捕捉现实世界系统的复杂性，并且比标准机制的计算要求更低。新的 RBFCM 的优势通过不同的例子进行了展示，并与标准机制进行了比较。