Model error modelling using a stochastic embedding approach with gaussian mixture models for FIR systems

Abstract:

In this paper a Maximum Likelihood estimation algorithm for error-model modelling using a stochastic embedding approach is developed. The error-model distribution is approximated by a finite Gaussian mixture. An Expectation-Maximization based algorithm is proposed to estimate the nominal model and the distribution of the parameters of the error-model by using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

Año de publicación:

2020

Keywords:

• Stochastic embedding
• Expectation-maximization
• Estimation
• Model errors
• Maximum likelihood
• Gaussian Mixture

Fuente:

scopus