Sentence examples for input belonging to from inspiring English sources

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.

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.

Consider a two-user memoryless AWGN broadcast channel (SNR1>SNR2) with signal power constraint P. The channel input belongs to a finite set X = { x 0, …, x M - 1 } ⊂ ℝ represented by an M-PAM constellation.

This review addresses cellular and molecular mechanisms that regulate the competition between two inputs belonging to different neuronal populations in innervating two contiguous but separate domains of the same target cell.

Posterior probability outputs by a teacher DNN for two inputs belonging to the same tied CD state are shown with state IDs for the five states having the highest posterior probability shown next to their corresponding bars.

The steps to determine the fuzzy rule-based interference are as follows:  Fuzzification: In Fuzzification, the crisp inputs are obtained from the selected input variables and then the degrees to which the inputs belong to each of the suitable fuzzy set are estimated.

The steps that determine the fuzzy logic system are as follows: Fuzzification: The process of getting the crisp inputs from the chosen input variables and estimating the degree to which the inputs belong to each of the appropriate fuzzy sets is termed as fuzzification.