Pattern Classification Using A Fuzzy Immune Network Model

It is generally believed that one major function of immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model(here, we call it the binary model) based on biological immune response network was proposed in our previous work. However, there are some problems like input and memory in the binary model. In order to improve the binary model, in this paper we propose a fuzzy immune network model. In the proposed fuzzy immune model, we add a normalization B cell layer for normalizing the large-scale antigen information on the base of the binary model. Meanwhile, a fuzzy AND operator(.AND.) and a normalization procedure called complement coding were employed in the proposed fuzzy immune model. Compute simulations illustrate that the proposed fuzzy model not only can improve the problems existing in the binary model but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories. (author abst.