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Experiment design for maximum-power model validation

โœ Scribed by Torsten Bohlin; Ludomir Rewo

Book ID
102637811
Publisher
Elsevier Science
Year
1980
Tongue
English
Weight
327 KB
Volume
16
Category
Article
ISSN
0005-1098

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โœฆ Synopsis

The paper considers the problem of input signal selection to maximize power of an Asymptotic Locally Most Powerful (ALMP) test. It" is shown that the input signal which maximizes the test power simultaneously yields maximum accuracy of identification if disturbances are Gaussian. For linear multivariable discrete-time systems described by transfer functions both the input signal optimality criterion and its gradient are derived. This allows input signal optimization by means of a gradient hill-climbing method. The theory is illustrated by the optimal experiment design for a zero-order system with additive coloured disturbances.

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โœ Torsten Bohlin ๐Ÿ“‚ Article ๐Ÿ“… 1978 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 887 KB

Optimal validation of stochastic dynamic models, e.g. by the Likelihood-Ratio test, generally requires other, overfitted models for comparison. This is avoided in a minimaxoptimal test applicable to general prediction-error models.