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Uncertainty identification by the maximum likelihood method

✍ Scribed by José R. Fonseca; Michael I. Friswell; John E. Mottershead; Arthur W. Lees


Publisher
Elsevier Science
Year
2005
Tongue
English
Weight
366 KB
Volume
288
Category
Article
ISSN
0022-460X

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📜 SIMILAR VOLUMES


On the uniqueness of maximum likelihood
✍ T Söderström 📂 Article 📅 1975 🏛 Elsevier Science 🌐 English ⚖ 426 KB

The maximum likelihood method of identification is a powerful tool for obtaining mathematical models of dynamic processes. To apply this method a loss function has to be minimized. The aim of the paper is an investigation of the local minimum points of this loss function for a common structure of a

Non-convergence of the approximate maxim
✍ V. Panuska 📂 Article 📅 1980 🏛 Elsevier Science 🌐 English ⚖ 205 KB

The question of non-convergence of the approximate maximum likelihood identification algorithm is discussed. It is pointed out that although there are systems for which in theory the algorithm does not converge to any finite limit, the computer implementation always gives results which settle at con

On the problem of ambiguities in maximum
✍ T. Bohlin 📂 Article 📅 1971 🏛 Elsevier Science 🌐 English ⚖ 1015 KB

When modelling a physical process, basic mathematical assumptions are often difficult to verify in advance. However, the applicability of certain statistical identification methods to a given data sample can be checked by testing the resulting model. Summary--This contribution derives new large-sam