提出了一种基于模糊免疫网络算法对火焰数字图像进行分类的研究方法。

The method based on fussy immune network algorithm was presented, by which the flame digital images can be classified better.

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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.

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采用免疫进化算法训练r BF网络，进一步缩小了标准进化算法搜索空间的范围，提高了算法的收敛速度。

Then immune evolutionary algorithm is used to train the RBF network, which reduces the searching space of canonical evolutionary algorithm and improves the convergence speed.

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