南京大学学报(自然科学版) ›› 2016, Vol. 52 ›› Issue (4): 746–.
李 慧1，陈红倩2*，马丽仪1，梁 磊1，孙 旸1
Li Hui1，Chen Hongqian2*，Ma Liyi1，Liang Lei1，Sun Yang1
摘要： 模糊认知图(fuzzy cognitive maps，FCM)是一个推理网络，使用循环有向图对知识进行表示与推理，通过模糊逻辑与神经网络的组合完成建模、分析、决策支持、预测等任务．针对模糊认知图的研究现状，从模型构建与学习算法、结构拓展和领域应用创新三个方面，针对近些年来的模糊认知图的算法改进进行了综述．在模型构建与学习算法方面，针对基于Hebbian的学习算法、基于群体的学习算法、基于Hebbian和演化类型混合的学习算法进行了综述，并对算法的主要特征和适用领域进行了描述．在结构模型拓展方面，重点综述了在有专家参与的决策支持领域、高度不确定的复杂系统、动态的领域环境和实时控制系统领域四种领域环境中，对模糊认知图的演化模型改进，并针对模糊认知图中各改进算法的优缺点进行了比较和综述．最后，综述了模糊认知图在各领域中的应用特点和应用案例．
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