A Classification Method Based on Modified Artificial Immune Net

A clustering method based on modified artificial immune is proposed to overcome the difficulties in computational effort, sampling requirements and the choice of compressing thresholds. To solve the problem on the compress parameter adjusting, a novel concept of double memories layers architecture was presented. Based on the architecture and improved cell-elimination laws, a new approach of cell-elimination process was developed. The ideas above aroused from quantum mechanics theory, the Schrdinger equation and the energy level were applied to the immune net. The simulation was performed by taking the data from UCI database. It proves that the classification accuracy of the proposed artificial immune system is improved, and better training speed is gained compared with the artifacial immune net method.