Abstract: This paper discusses how to change the division between immature Dendritic Cells and mature Dendritic Cells in the traditional Dendritic Cell Algorithm by using the fuzzy sets theory. The traditional Dendritic Cell Algorithm attempts to find a notable boundary between the SmDC and the MDC. However, it has been proved that there is no clear division between the SmDC and the MDC. Therefore, certain misjudgments must exist in such a division method based on the boundary judgment. The model proposed in this paper is a new algorithm model built by using the fuzzy sets theoretical framework and the Dendritic Cell Algorithm. Emulation experiments show that the fuzzy Dendritic Cell Algorithm can reduce the false alarm rate to a certain extent. For scattered data sets, this algorithm is more effective and accurate. The method proposed in this paper is more effective and accurate without being affected by the sorting.

Key words: fuzzy set, Dendritic Cell Algorithm（DCA）, crisp partition, linguistic variables

摘要： 利用模糊集合论的理论来改变传统的树突状细胞算法中对半成熟树突状细胞和成熟树突状细胞的清晰化划分问题。传统的树突状细胞算法的基于边界判断的清晰化划分方式对数据的排序敏感，并且存在着一定比例上的误判。提出的模型是基于模糊集合论框架下结合树突状细胞算法建立起来的一种全新的算法模型，实验证明基于模糊树突状细胞算法的实验结果一定程度上减低了误报率，不受排序的影响，更加有效和精确。

关键词: 模糊集合, 树突状细胞算法, 清晰划分, 语言变量