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With randomly generated weights between input and hidden layers, a random vector functional link network is a universal approximator for continuous functions on compact sets with fast learning property.
This fact may be used to prove minimization results for continuous convex functionals, in the same way that the Bolzano Weierstrass theorem is used for continuous functions on Rd.
For continuous functions f the value of f(t), for t in the interval, changes only slightly, so it must be very close to f(t).
We compute the leading term of the asymptotics of Trf as ε→0 for continuous functions f vanishing sufficiently fast near zero.
We interpret the McAfee Reny condition as a much stronger version of injectiveness of belief functions and prove genericity by arguments similar to those used to prove the classical embedding theorem for continuous functions.
The literature on inequalities for continuous functions and their applications is vast (see [1 8]).
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The main reason for continuous functioning of indigenous knowledge is that herders put the knowledge to continuous use (Grice and Hodgkinson 2002).
If (beta>0), (alpha+beta>0), then the equation I_{0+}^{alpha}I_{0+}^{beta}f(x)=I_{0+}^{alpha+beta}f(x), is satisfied for continuous function f.
Thus, for any θ on interval ( φ ( c ), β 2 ), there exists μ ⋆ , 0 < μ ⋆ < c such that φ = θ, i.e., (3.5) holds when μ = μ ⋆ , which follows directly from the intermediate value theorem for continuous function.
This is because the above mutation operator was developed for continuous function optimisation, and it does not consider the discrete nature of the qualitative model space.
We find bounds for a Gronwall-Bihari type inequality for piecewise continuous functions.
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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