关键词: 人工免疫网络, 动态自适应, 小生境, 模糊聚类

Abstract: To improve the data processing capabilities of the artificial immune network,a dynamic adaptive immune network was proposed based on aiNet.The fuzzy C-means was used to cluster part of the original data,on which a certain data of each category were selected respectively to be the initial antibodies.Thus the distribution information of the original data was obtained,which could reduce the blindness led by initial antibodies selection randomly.To avoid the inbreeding during the immune evolution processing,the fitness function based on niche was introduced which was used to effectively measure the affinity between antibody and antigen.The selection of clone antibody and the threshold of network suppression were dynamically controlled in the process of network evolution,which made them change dynamically along with the evolution of network.The adaptability of the proposed algorithm was improved.The experiments were used to analyze and testify the efficiency and accuracy of the proposed algorithm,and the results showed that the proposed algorithm had higher classification accuracy,and the network convergence rate was greatly increased comparing to several other methods.

Key words: artificial immune network, dynamic adaptive, niche, fuzzy clustering