关键词: 人工免疫网络, 模式识别, 免疫算子, 智能互补策略, K-means聚类

Abstract: There are redundant antibodies after training in self-organization Antibody Network (soAbNet) and its network performance is instable. In order to improve the performance of soAbNet, a hybrid immune diagnosis method was proposed based on intelligence complementary strategy. Immune operator was introduced into soAbNet, which consisted of two components: vaccination and immunoselection. Vaccines obtained through K-means algorithm were taken as initial antibodies in immune operator, and immune network architecture was optimized by immunoselection. The experimental results on Iris dataset demonstrate that, the proposed hybrid immune algorithm sufficiency makes use of prior knowledge and learns data characteristics effectively, and the diagnostic accuracy and data enrichment rate are higher compared with soAbNet.

Key words: artificial immune network, pattern recognition, immune operator, intelligent complementary strategy, K-means clustering