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An adaptive neural controller is designed by utilizing the approximation technique of neural network.
For this purpose, utilizing the approximation s i,j ≫ 1 / γ j transforms Equation 28 into d L 0 γ dr j = dl 0 γ j dr j ≈ - 1 2 γ j - 2 A 0 + γ j - 1 B 0 + C 0. (53).
However, by utilizing the approximation of the conditional expectation of the concentration, we can still use a proportional-like system noise.
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Testing the probability of shared polymorphisms arising via gene conversion vs. independent mutation utilized the approximation of the hypergeometric distribution (implemented in DNASP; method described in [ 26]).
One solution to overcome this limitation is utilizing the mixture approximation.
However, by utilizing the following approximation [41]: log left| {mathbf{I} + {mathbf{HQ}}{mathbf{H}^{H}}} right| = {text{Tr}}left({mathbf{HQ}}{mathbf{H}^{H}}right) + {mathcal{O}}left left| {{mathbf{HQ}}{mathbf{H}^{H}}} right|right), (30).
Further, the artificial neural network ARCH process (ANN-GARCH) developed by Donaldson and Kamstra (1997) augments the GJR model with multi-layer perceptron-based neural network architecture with logistic squashing functions to capture nonlinearity by utilizing the universal approximation property (Cybenko 1989) of ANN models.
Utilizing the Boussinesq approximation (which is valid because the inertial effects of the density stratification are negligible, the dominant term multiplying the inertia terms is the density of the base fluid that exceeds by far the density stratification), the momentum equation can be written as: 1 P r ( ∂ U ∂ t + U ⋅ ∇ U ) = − ∇ p + ∇ 2 U + R m k ^ − R a T k ^ + R n ϕ k ^ + R b L b n k ^ (2).
By utilizing the SINR approximations, a low-complexity power control algorithm for sum SE maximization is proposed.
First-principles calculations were performed within the density functional theory (DFT) utilizing the generalized gradient approximation (GGA 30,31,32,33,34.
Density functional theory calculations were made within the pseudopotential approach of VASP utilizing the general gradient approximation for the exchange correlation functional.
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