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Differential Neural Network Identification for Homogeneous Dynamical Systems ⋆

Abstract : In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.
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https://hal.inria.fr/hal-02278726
Contributor : Andrey Polyakov <>
Submitted on : Wednesday, September 4, 2019 - 3:27:21 PM
Last modification on : Friday, December 11, 2020 - 6:44:08 PM
Long-term archiving on: : Thursday, February 6, 2020 - 1:51:23 AM

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Mariana Ballesteros, Andrey Polyakov, Denis Efimov, Isaac Chairez, Alexander Poznyak. Differential Neural Network Identification for Homogeneous Dynamical Systems ⋆. NOLCOS 2019 - 11th IFAC Symposium on Nonlinear Control Systems, Sep 2019, Vienna, Austria. ⟨hal-02278726⟩

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