Sentence examples for using a nonlinear function from inspiring English sources

Hidden layer The hidden layer transforms the data from the input space to the hidden space using a nonlinear function.

In [5], an overall stereo quality metric was proposed through the combination of image quality with disparity quality using a nonlinear function.

To transform the nonlinearly separable data into linearly separable data, the data are mapped in the form ϕ : R n → H using a nonlinear function ϕ into a higher dimensional feature space which is also a Hilbert space.

We minimized the errors introduced by the Kalman filter by using robust methods to represent the orientation and time update of the orientation, and decreased the nonlinearity be using a nonadditive error state Kalman filter in which the error state is combined with the real state using a nonlinear function (see the transfer of the orientation error in Figure 8).

A two-channel transmission method is used to exemplify the suggested technique where the secret message is encoded using a nonlinear function of both the chaotic states and the adjustable parameter of the chaotic system that acts as a secret key.

T1 and T2 maps were calculated assuming a monoexponential signal decay and by using a nonlinear function, least-squares curve fitting on the relationship between changes in mean signal intensity within a region of interest (ROI) to TR and TE.

We also used a nonlinear function with a natural cubic spline for weekly mean temperature and weekly mean relative humidity.

In the first step, in the hidden layer, input variables x i = (x i 1,..., x ip ) (j=1,…,p markers) are combined for each neuron (k=1,…, S neurons), using a linear function,, and subsequently transformed using a nonlinear activation function, yielding a set of inferred scores,, where g k is the activation function that maps the inputs into the real line in the closed interval [−1,1].

Incorporating nonlinearity in the predictor, however, by using a nonlinear kernel function, resulted in a prediction accuracy that was slightly less than in linear SVR, but this prediction still outweighed the accuracy of the MLR model.

Li et al. [19] studied some generalized minimax theorems for set-valued mappings by using a nonlinear scalarization function.

This new technique is based on the modification of the classical diffusion rule by using a nonlinear sigmoidal function.