Abstract
As an engineering application of a biological immune system, an immune feedback control law featuring rapid response to foreign materials and quick stabilization of the immune system is applied to controlling the learning of multi-layer neural networks. Applying the immune feedback control law to the generalized δ-rule, the immune-feedback-based learning rule is derived. The stability condition of the proposed learning rule for the 2-layer neural networks is analytically described. To investigate the feasibility of the proposed learning rule to the multi-layer neural networks, simulation studies of identifying nonlinear functions using the 4-layer neural networks and the sandglass-type neural networks are carried out. Simulation results show both the effectiveness and characteristics of the immune feedback-based learning rule.
