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where y j ∈ [0,1] is the prediction probability of the input belonging to the j-th class and q j is the true probability.
The operation of the fuzzification calculates the degrees for each evaluated parameter (input) belonging to the three membership functions, e.g., for RC_S this operation calculates {(upmu _{N})(RC_S), (upmu _{M} RC_S), (upmu _{F})(RC_S),} with (upmu _{N})(RC_S), (upmu _{M} RC_S) and (upmu _{F})(RC_S) the membership degrees of fuzzy sets N, M, and F, respectively.
where x ( t ) ∈ R n is the state vector, φ ( s ) ∈ R n is the vector-valued initial function, v ( t ) ∈ R p is the disturbance input belonging to L 2 [ t 0, ∞ ), u ( t ) ∈ R q is the control input, w ( t ) is a one-dimensional zero-mean Wiener process on a probability space ( Ω, F, P ) satisfying E { d w ( t ) } = 0, E { d w 2 ( t ) } = d t, (2).
The pseudo-code of the above algorithm with an additional leave-one-out verification step is as follows: The Matlab (TM) classify function provides a posterior probability of the input belonging to a certain classification group.
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Frequency domain conditions guaranteeing an L2 output provided the system input belongs to L2 are also presented.
Fuzzification refers to transformation of crisp inputs into a membership degree, which expresses how well the input belongs to the linguistically defined terms.
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The SVM takes a set of input data with corresponding class labels, and predicts to which class a new input belongs.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com