An Improved Artificial Immune Network Model

Wei-Dong SUN  Zheng TANG  Hiroki TAMURA  Masahiro ISHII  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E87-A   No.6   pp.1632-1640
Publication Date: 2004/06/01
Online ISSN: 
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
immune response,  fuzzy immune theory,  immune network,  pattern classification,  pattern recognition,  

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It is generally believed that one major function of the immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in the 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 immune network) based on the biological immune response network was proposed in our previous work. However, there are some problems like that input and memory were all binary and it did not consider the antigen diversity of immune system. To improve these problems, in this paper we propose a fuzzy immune network model by considering the antigen diversity of immune system that is the most important property to be exhibited in the immune system. As an application, the proposed fuzzy immune network is applied to pattern recognition problem. Computer simulations illustrate that the proposed fuzzy immune network model not only can improve the problems existing in the binary immune network but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories.