Given:
- a function
LaTeX Math Inline |
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body | --uriencoded--y%5e*(x, %7B\bf p%7D) |
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of the argument and set of model parameters LaTeX Math Inline |
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body | --uriencoded--%7B\bf p%7D = \%7B p_m\%7D_%7Bm = 1..M%7D = \%7Bp_1, p_2, ... p_M\%7D |
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- a training data set:
LaTeX Math Inline |
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body | --uriencoded--\%7B (x_k, y_k)\%7D_%7Bk = 1..N%7D = \%7B (x_0, y_0), (x_1, y_1), ..., (x_N, y_N) \%7D |
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the matching procedure assumes searching for thee specific set of model parameters
LaTeX Math Inline |
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body | --uriencoded--%7B\bf p%7D |
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to minimize the goal function: LaTeX Math Block |
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F({\bf p}) = \sum_{k=1}^N \, \Psi(y^*(x_k) - y_k) \rightarrow \textrm(min) |
where
is the discrepancy distance function.Most popular choices are
LaTeX Math Inline |
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body | --uriencoded--\Psi(z) = z%5e2 |
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and LaTeX Math Inline |
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body | --uriencoded--\Psi(z) = %7Cz%7C |
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.
See also
...
Human / Science / Formal Science / System Science / System Model