In order to prevent neural network learning from getting into local extreme point, artificial immune network algorithm was used to optimize neural network's parameters.

为了避免神经网络的学习过程陷入局部极值点，采用人工免疫网络优化神经网络的参数。

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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.

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。

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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.

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。

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