在此基础上，设计和实现了人工免疫网络算法，并应用该算法成功解决了一个模式识别和数据聚类问题。
We design and implement the artificial immune network algorithm, and successfully apply this algorithm in solving a pattern recognition problem and a data clustering problem.
最后研究了基于克隆选择算法和人工免疫网络的模拟故障诊断方法。
The paper investigates the analog circuit fault diagnosis method based on clonal selection algorithm and artificial immune network.
在此基础上， 对现有的基于人工免疫的网络入侵检测算法进行分析比较，针对线性时间检测器生成算法的不足作了改进。
Then it analyzes and compares the existing network intrusion detection algorithm based on artificial immune, and improves the linear time detector generating algorithm for the deficiency.
把人工免疫系统和神经网络系统的信息处理机制引入到CSA提出了免疫克隆选择算法。
By introducing the information processing mechanism of artificial immune systems and neural network to CSA, an immune clonal selection algorithm (ICSA) was proposed.
目前对于基于人工免疫系统的数据挖掘技术的研究，多使用的是基本的免疫算法，而忽略了免疫网络模型等其它免疫机理所具有的独特优势。
On the other hand, the artificial immune system in data mining mainly adopts the basic immune algorithms, which ignore the unique characters of the other immune mechanisms such as the immune networks.
本文在深入分析免疫机理工作方式的基础上，提出了两种基于人工免疫网络的分类算法。
Based on the in-depth analysis of the immune mechanism, two kinds of classification algorithm based on artificial immune network are proposed in this paper.
当数据集聚类边界不清晰或存在噪声干扰时，人工免疫网络聚类算法通常无法获得有效的聚类划分。
Artificial immune network clustering is often ineffective when there is noise or undefined cluster boundary in the data.
当数据集聚类边界不清晰或存在噪声干扰时，人工免疫网络聚类算法通常无法获得有效的聚类划分。
Artificial immune network clustering is often ineffective when there is noise or undefined cluster boundary in the data.
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