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Qualitative optimization of Fuzzy Causal Rule Bases using Fuzzy Boolean Nets

机译:基于模糊布尔网络的模糊因果规则库定性优化

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摘要

Fuzzy Causal Rule Bases (FCRb) are widely used and are the most important rule bases in Rule Based Fuzzy Cognitive Maps (RB-FCM). However, FCRb are subject to several restrictions that create difficulties in their creation and completion. This paper proposes a method to optimally complete FCRb using Fuzzy Boolean Net properties as qualitative universal approximators. Although the proposed approach focuses on FCRb, it can be generalized to any fuzzy rule base.
机译:模糊因果规则库(FCRb)被广泛使用,并且是基于规则的模糊认知图(RB-FCM)中最重要的规则库。但是,FCRb受到一些限制,这些限制在其创建和完成方面造成了困难。本文提出了一种利用模糊布尔网络性质作为定性通用逼近器来最优完成FCRb的方法。尽管所提出的方法侧重于FCRb,但可以将其推广到任何模糊规则库。

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