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Also we have calculated the uniform errors between their derivatives (see Table 2).
The uniform errors between monotonic fractal interpolants and their derivatives are given in Table 4 to show the importance of our rational cubic IFS (11).
The uniform errors then will be transferred back to the original domain by inverse marginal CDFs, and finally form the wind power scenarios.
We have calculated the uniform errors between this original function Φ in Figure 1(a) and the rational cubic FIFs in Figures 1(b - f) (see Tab - f).
The BFR for a constant block size N is calculated using a binomial distribution of uniform errors as: BFR = P error > t = ∑ i = t + 1 N N i BE R i 1 − BER N − i (3).
Table 2 Uniform errors between Φ and rational fractal interpolants, and their derivatives Rational cubic FIF Uniform distance with Figure 1(a) Derivative of rational cubic FIF Uniform distance with Figure 2(a) Figure 1(b) 0.0401 Figure 2(b) 0.5042 Figure 1(c) 0.1901 Figure 2(c) 1.7129 Figure 1 d) 0.1646 Figure 2(d) 1.8654 Figure 1 e) 0.0379 Figure 2 e) 1.0421 Figure 1(f) 0.3154 Figure 2(f) 2.9245.
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These nonlinear codes are capable of providing uniform error detecting coverage independently of the error distributions.
Uniform error distribution is observed for every position, even though error positions are not i.i.d.i.d
Random losses, obtained by applying a uniform error model to the wireless channel.
Accelerated convergence and more uniform error can be achieved without additional computations during runtime.
The uniform error distribution equal to the calculated standard error was added to the model coefficient estimates.
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