High dynamism is provided by integer parameters which can change at each basic iteration and boolean parameters which can even change within the iteration.
When the convergence is achieved, e.g., the parameter estimates do not change as the iteration continues, the estimation procedure is suspended, and the parameter estimates obtained in the current iteration are considered to be the final result.
(Figure 1b) If we now keep the number of LTP cells in a sphere constant (say, 4) and with each iteration change the total cells generated per sphere from 1000 (i) to 2000 (ii) to 4000 (iii), we find that the elevation of the growth curve is affected but not its slope (as seen by an unchanged fold expansion).
Although the SAGE algorithm can change the estimates with more iterations, such biases may still exist in the final estimation results.
This defined error probability of estimated weight is plotted as a function of the number of iterations where after each 10 iterations, we change the number of malicious users (30, 60, and 90 out of 195).
The planning center can easily calculate for any t the sum of their announced MRSs to change the allocation at the next iteration (t+dt. ) It is supposed that the center can get an exact value of MRT.
In order to terminate at a finite number of iterations, we change the stop criterion, Step 3 in Algorithm 1, to get the following algorithm.
The increment value changed in the iteration procedure is selected by using the optimization method.
Figure 3 illustrates a neural network using the backpropagation algorithm whereby the weight is changed as the iteration increases, thereby reducing the error and getting closer to the desired target (Al-Faruk et al. 2010; Juang et al. 1998).
Only variables which are not supposed to be changed during the iteration procedure will be copied by value.
The LVQ1 network learns all the possible variations for a particular data set and in order to obtain the optimum iteration, we continuously changed the number of iteration steps from a small number to a large one with continuous observation of classification of the data.
