Stability analysis of dynamic multilayer neuro identifier

In the paper, dynamic multilayer neural networks are used for nonlinear system on-line identification. A passivity approach is applied to access several stability properties of the neuro identifier. The conditions for passivity, stability, asymptotic stability and input-to-state stability are established. We conclude that the commonly-used backpropagation algorithm with a modification term which is determined by off-line learning may make the neuro identification algorithm robustly stable with respect to any bounded uncertainty.