混合模糊认知图

传统的模糊认知图(FCM)仅限于表示单调的或对称的因果关系,不能模拟原因节点间的与或组合关系.针对现有FCM模型的缺陷,提出了混合模糊认知图(HFCM).HFCM以单前件模糊规则拓展传统的因果模糊测度,增强了FCM的语义信息和模拟能力;使用WOWA或OWA集结算子融合因果推理结果,模拟原因节点间的各种与或关系.与传统FCM相比,HFCM具有更强的认知能力.与基于规则的FCM相比,HFCM规则库的规模及复杂度由几何级降至算术级,解决了组合激增问题,提高了FCM的表示与推理性能.HFCM兼有数值型FCM和语言型FCM的优点.

Hybrid fuzzy cognitive maps

The conventional Fuzzy Cognitive Maps(FCM) can only represent monotonic or symmetric causal relationships,but can not simulate the AND/OR relationships among the antecedent nodes.The Hybrid Fuzzy Cognitive Map(HFCM) is proposed to eliminate the drawbacks of the existing FCM models.The HFCM represents the casual relationships with single-antecedent fuzzy rules to enhance linguistic information and simulative capability of FCM,and simulates various AND/OR relationships among the antecedent nodes by aggregating causal inference results with Weighted Ordered Weighted Averaging(WOWA) or Ordered Weighted Averaging(OWA) operators.Compared with the conventional FCM,the HFCM has more powerful cognitive capability.Compared with the Rule Based Fuzzy Cognitive Map,the HFCM avoids the combinatorial rule explosion problem as the scale and complexity of its rule base are reduced from the geometrical level to the arithmetical level,and improves the representation and inference performance of FCM.The HFCMs combine the advantages of numeric FCMs and linguistic FCMs.