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In this work, dynamic multilayer neural networks are used for nonlinear system online identification. The 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 offline learning may make the neuro identification algorithm robustly stable with respect to any bounded uncertainty.