Adaptive systems: principles of identification
The object to be identified; it is typically defined via the (measurable) signals we can obtain from it.
The parameterized model class out of which we want to select a member to represent the object of interest. The description of this model class reflects prior knowledge that we have about the system to be identified.
A criterion with which we are able to make a distinction between different members of the model class. This criterion must be defined in terms of the measurable signals and again may reflect our prior knowledge. Alternatively we want an identification algorithm that maps the measurable signals into a preferred parameter, defining a member of the model class.
KeywordsEquation Error Adaptive System Information Matrix Little Mean Square Dead Zone
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