Sentence examples for mean weight behavior from inspiring English sources

The study investigates the MSE error, mean weight behavior, stationary points, misadjustment error, and stability conditions.

Figure 16 Mean weight behavior: Second NN block (theory and simulation vs. iterations).

Figure 15 Mean weight behavior: First NN block (theory and simulation).

A region for this weight is obtained and the mean weight behavior of the algorithm using this cost function is achieved.

This confirms the validity of the different assumptions made in Sections 4.1 and 4.2. Figure 14 Mean weight behavior: Matrix W (theory and simulation).

Following Equation (44), the mean weight behavior can be expressed as function of the initial condition as: E W n = W 0 I − 2 μ Ω 2 R g X g X n + 2 μ H Ω R g X g X ∑ p = 0 n − 1 I − 2 μ Ω 2 R g X g X p (46).

Figure 7 Mean weight transient behavior - Linear adaptation case.

Here we give analytical expressions for the mean squared error (MSE), explore the stationary points of the algorithm, evaluate the misadjustment error due to weight fluctuations, and derive recursions for the mean weight transient behavior during the learning process.

This means that while adults and children can show similar mean weights, and behavior consistent with integration of cues, children do not show variance reduction given multiple cues.

The mean weight recursions and the MSE transient behaviors (Figures 6,7) have been estimated over 20 Monte Carlo (MC) simulations and compared to the theoretical derivations (Equations (19) and (41)).

A more practical approach, perhaps leading to a larger degree of change of behavior among the participants, might have generated a larger mean weight loss.