Sentence examples for highly nonlinear data from inspiring English sources

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The RBF SVR method has three model parameters: the already discussed ε and C and the extra kernel function parameter γ, which determines the degree of nonlinearity: setting γ to high values results in a highly nonlinear data model [ 39].

It can classify highly nonlinear data using kernel function.

Thus, for highly nonlinear data distribution, these kernel methods are more suitable for low-resolution and illumination variation conditions.

However, neural networks represent highly nonlinear data and may exhibit more complexity than what PCA or MDS are designed to detect [4].

Forth, the highly nonlinear data are comparatively analysed with KdV-NLFT as well as with the conventional fast Fourier transform (FFT) and the Hilbert-Huang transform (HHT).

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One of the major advantages of ANN is efficient handling of highly nonlinear relationships in the data when the exact nature of such relationship is unknown [5].

One of the major advantages of ANN is efficient handling of highly nonlinear relationships in the data, even when the exact nature of such relationship is unknown.

However, as a linear algorithm, DSRC cannot handle the data with highly nonlinear distribution.

The TS fuzzy models are universal approximators both of a function and its derivative, so it allows modeling highly nonlinear systems based on input/output data.

While facilitating an efficient and scalable parameter estimation, a model decomposition involving the linear approximation of the time integral of each rate equation may not result in a high-quality estimate, especially when the model of interest is highly nonlinear or the given time series data are sparse.

Artificial neural networks, once successfully trained, offer an alternative way to obtain reliable and more accurate results for the determination of crude oil PVT properties, because it can capture highly nonlinear behavior and relationship between the input and output data as compared to linear and nonlinear regression techniques.

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