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中文摘要：
由有免疫力的网络理论启发了，一个适应异例察觉范例基于人工的有免疫力的网络，作为 APAI 参考了，被建议。范例的实现包括：开始，第一是创造起始的抗体网络；通过听说每训练抗原，然后，抗体网络被发展；由最佳的抗体更新了。最后，异例察觉过程被 k 的多数投票最近完成在网络的邻居抗体。实验在我们的学习使用了著名声纳基准数据集，它从 UCI 机器学习数据库被拿。APAI 的获得的察觉精确性是 97.7% ，它关于在为这个问题的文学的另外的分类应用是很有希望的。除了它的非线性的分类性质， APAI 拥有象同种细胞的选择那样的生物有免疫力的网络性质，有免疫力的网络，；有免疫力的记忆，能被用于模式识别，分类，；等等。
英文摘要：
Inspired by the immune network theory, an adaptive anomaly detection paradigm based on artificial immune network, referred as APAI, is proposed. The implementation of the paradigm includes： initially, the first is to create the initial antibody network; then, through the learning of each training antigen, the antibody network is evolved and updated by the optimal antibodies. Finally, anomaly detection process is accomplished by majority vote of the k nearest neighbor antibodies in the network. The experiments used the famous Sonar Benchmark dataset in our study, which is taken from the UCI machine learning database. The obtained detection accuracy of APAI was 97.7%, which was very promising with regard to the other classification applications in the literature for this problem. In addition to its nonlinear classification properties, APAI possesses biological immune network properties such as clonal selection, immune network, and immune memory, which can be applied to pattern recognition, classification, and etc.
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