Nonlinear System Identification Using Additive Dynamic Neural Networks

Abstract In this work additive dynamic neural models are used for the identification of nonlinear plants in on-line operation. In order to accomplish this task a gradient parameter adaptation method based in sensitivity analysis is formulated taking into account that the parameters of the model are arranged in matrix form. This methodology is applied to several nonlinear systems in simulation and with a real dataset to verify its performance.

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