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Table 2 Functions used to generate artificial datasets for regression

From: Novel sensitivity method for evaluating the first derivative of the feed-forward neural network outputs

Function

Exact derivatives

\({R}_{1}:\,y={x}_{1}^{4}+2{x}_{2}^{3}+3\sqrt{{x}_{3}}\)

\(\frac{\partial y}{\partial {x}_{1}}=4{x}_{1}^{3}; \frac{\partial y}{\partial {x}_{2}}=6{x}_{2}^{2}; \frac{\partial y}{\partial {x}_{3}}=\frac{3}{2\sqrt{{x}_{3}}}\)

\({R}_{2}:\,y=\mathrm{sin}(\pi {x}_{1})+{e}^{{x}_{2}}+{x}_{3}^{2}\):

\(\frac{\partial y}{\partial {x}_{1}}=\pi \mathrm{cos}\left(\pi {x}_{1}\right); \frac{\partial y}{\partial {x}_{2}}={e}^{{x}_{2}}; \frac{\partial y}{\partial {x}_{3}}=2{x}_{3}\)

\({R}_{3}:\,y=\mathrm{sin}(\pi {x}_{1})+{e}^{{x}_{2}}+{x}_{3}^{2}+0.00001{x}_{4}\)

\(\frac{\partial y}{\partial {x}_{4}}=1e-5\)