本文借鉴模糊数学思想提出两种改进的否定选择算法，分别适用于不同的入侵检测环境。
This paper proposes two modified negative selection algorithm through the reference of fuzzy mathematics, applied to different intrusion detection environment.
为了提高人工免疫系统中抗体生成速度，基于免疫系统的混沌特征，提出了混沌否定选择算法。
To increase antibody generating speed in artificial immune system, a chaos negative selection algorithm was presented which is based on the chaotic character of immune system.
目前在基于人工免疫的入侵检测系统中，成熟检测器的耐受和生成过程主要采用否定选择算法实现。
In the artificial immune based intrusion detection systems, mature detectors complete the tolerance and generation process mainly by negative selection algorithm currently.
基于人工免疫系统带变异的否定选择算法的思想，对滑坡监测数据进行处理，根据部分匹配原则产生检测器，寻找滑坡突变点。
The shortcoming of binary negative selection algorithm and the excellence of real value coding based on the negative selection mechanism of artificial immune systems is analysed.
使用了一种改进的否定选择匹配算法来检测异常行为。
A matching algorithm based on the negative selection for anomaly detection was presented in this paper.
使用了一种改进的否定选择匹配算法来检测异常行为。
A matching algorithm based on the negative selection for anomaly detection was presented in this paper.
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