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.

Immune system protects living body from an extraordinarily large variety of bacteria, viruses, and other pathogenic organisms. Based on immunological principles, new computational techniques are being developed, aiming not only at a better understanding of the system, but also at solving engineering problems. Our overall goal for this paper is twofold: to understand the real immune system from the information processing perspective, and to use idea generated from the immune system to construct new engineering application. As one example of the latter, we propose an artificial immune system architecture inspired by the human immune system and apply it to pattern recognition. We test the proposed architecture by the simulations on arbitrary sequences of analog input pattern classification and binary input pattern recognition. The simulation results illustrate that the proposed architecture is effective at clustering arbitrary sequences of analog input patterns into stable categories and it can produce stronger noise immunity than the binary network .

This paper describes a new model of multiple-valued immune network based on biological immune response network. The model of multiple-valued immune network is formulated based on the analogy with the interaction between B cells and T cells in immune system. The model has a property that resembles immune response quite well. The immunity of the network is simulated and makes several experimentally testable predictions. Simulation results are given to a letter recognition application of the network and compared with binary ones. The simulations show that, beside the advantages of less categories, improved memory pattern and good memory capacity, the multiple-valued immune network produces a stronger noise immunity than binary one.