Regularization networks for inverse problems: A state-space approach

@article{Nicolao2003RegularizationNF,
  title={Regularization networks for inverse problems: A state-space approach},
  author={G. Nicolao and G. Ferrari-Trecate},
  journal={Autom.},
  year={2003},
  volume={39},
  pages={669-676}
}
Linear inverse problems with discrete data are equivalent to the estimation of the continuous-time input of a linear dynamical system from samples of its output. The solution obtained by means of regularization theory has the structure of a neural network similar to classical RBF networks. However, the basis functions depend in a nontrivial way on the specific linear operator to be inverted and the adopted regularization strategy. By resorting to the Bayesian interpretation of regularization… Expand
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