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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… Click to show full abstract
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.
Keywords:
model;
embedding approach;
using stochastic;
error;
stochastic embedding;
modelling using
Journal Title: IFAC-PapersOnLine
Year Published: 2020
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Journal Title: IFAC-PapersOnLine
Year Published: 2020
Link to full text (if available)
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recommendations!