Model Identification
Updated 2026-08-22
INTRODUCTION
English translation pending.
CORE DEFINITION
Model identification, or system identification, estimates the structure and parameters of a system from measurements of its inputs and outputs. The core proposition is that a system's internal mechanism can be reverse-engineered by perturbing it with known inputs and observing the responses, provided the inputs excite the relevant behaviour. The key qualification is that the identified model is an approximation: unmodeled dynamics and noise remain, and poorly designed input signals leave parameters unidentifiable.
SCAFFOLDING EFFECT
Reduce cognitive load
- Excitation design: choose input signals that actually excite the behaviour you want to identify. - Structure choice: select a model family such as ARX or state space before estimating parameters. - Validation split: check the fitted model on independent data rather than the data used to fit it.
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If a system's response to known inputs is stable and sufficiently varied, the mapping from input to output constrains the internal parameters tightly enough to estimate them. Estimation methods such as least squares then recover the parameter values that best explain the observations. The result is only as informative as the excitation: inputs that never probe a mode leave its parameters unconstrained.
MINIMUM ACTION
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/System_Identification%3A_Theory_for_the_Userverified
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