Abstract
nspired by the theory of biological immune receptor editing/revision, an improved artificial immune system model is proposed. Different from generic model, the improved model does not need to set the detectors detection radius, but it gives the detector a certain degree of learning ability through receptor editing and receptor revision. This makes the detector has a capability to adjust the detection position and detection radius automatically. Experimental results show that the improved model achieves better detection performance than generic model.
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