Stability analysis of dynamic multilayer neuro identifier

In this paper, dynamic multilayer neural networks are used for nonlinear system on-line identification. 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 robust stability with respect to any bounded uncertainty.