Identification of Vehicle Engine at Idle Speed via Neural Networks
نویسنده
چکیده
In this paper, dynamic neural networks are used for engine model at idle speed 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.
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