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The linearization type of is a record type with two fields.
The expansion into a series gives a linearization of the Einstein field equations.
The behavior on the center manifold is generally not determined by the linearization and thus more difficult to study.
Feedback linearization is a common approach used in controlling nonlinear systems.
The final equation for the linearization of a function at is:
A common use of linear models is to describe a nonlinear system by linearization.
Typical exact array access analysis include linearization and atom images.
This method is called linearization and is a very powerful tool.
Therefore when dealing with such fixed points one can use the simpler linearization of the system to analyze its behaviour.
Python creates a list of a classes using the C3 linearization algorithm.
In non-harmonic cases, restrictions on the speed may lead to accurate linearization.
This "linearization" goes usually under the name of cohomology.
Probably the calibration routine simply sets the coefficients for the linearization equation.
In, short, linearization approximates the output of a function near .
Notice: (token list or "string") as the only linearization type.
Feedback linearization can be accomplished with systems that have relative degree less than .
Such 'linearization' establishes an 'as if'budget constraint for an individual.
Linearization and resampling are widely used techniques for data from complex sample designs.
That is, a distorted version of the linearization gives the original dynamics in some finite neighbourhood.
Eliminating the need for linearization also provides advantages independent of any improvement in estimation quality.
The goal of feedback linearization is to produce a transformed system whose states are the output and its first derivatives.
In higher dimensions, the derivative of a function at a point is a linear transformation called the linearization.
The idea of 'ignoring the nonlinear part' is thus encapsulated in this linearization procedure.
In mathematics linearization refers to finding the linear approximation to a function at a given point.
In microeconomics, decision rules may be approximated under the state-space approach to linearization.