Test for local structural identifiability of high-order non-linearly parametrized state space models
Automatica (Journal of IFAC)
On the identifiability and distinguishability of nonlinear parametric models
M2SABI Proceedings of the 1st IMACS-IFAC symposium on Mathematical modelling and simulation in agriculture and bio-industries
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Differential Equations Computing and Modeling
Differential Equations Computing and Modeling
Parametric and nonparametric curve fitting
Automatica (Journal of IFAC)
Paper: Identifiability and distinguishability concepts in electrochemistry
Automatica (Journal of IFAC)
Identifiability of homogeneous systems using the state isomorphism approach
Automatica (Journal of IFAC)
Information Sciences: an International Journal
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Identifiability becomes an essential requirement for learning machines when the models contain physically interpretable parameters. This paper presents two approaches to examining structural identifiability of the generalized constraint neural network (GCNN) models by viewing the model from two different perspectives. First, by taking the model as a static deterministic function, a functional framework is established, which can recognize deficient model and at the same time reparameterize it through a pairwise-mode symbolic examination. Second, by viewing the model as the mean function of an isotropic Gaussian conditional distribution, the algebraic approaches [E.A. Catchpole, B.J.T. Morgan, Detecting parameter redundancy, Biometrika 84 (1) (1997) 187-196] are extended to deal with multivariate nonlinear regression models through symbolically checking linear dependence of the derivative functional vectors. Examples are presented in which the proposed approaches are applied to GCNN nonlinear regression models that contain coupling physically interpretable parameters.