

![0_image_0.png](0_image_0.png)

Now, the possible question may come in to the mind how the sensitivity of first layer to those parameters is. To answer this query, in other process, simultaneously α and β are changed over 10 iterations (fig.8.). In a scaled coordination, it can be probed that the rate of α β for the lugeon data set and for laminar state is near to 1, even if α takes a little bigger value. This feature expresses response of overall system to the current wave of data, is more depends on the pervious state of society than to the government reaction. Another interesting result of such accomplishing is that the behavior of the system in large values of α and β : in despite of some anomalies, for large digits system doesn't consider other disorder parameter and fall in to the disordering way. In second situation instead of NFIS, we employ RST upon this assumption that the government based on history, experience and other like fashions in the world, can has ability to elicitation of relatively approximated rules of the observed and distinguished behaviors (by transferring of attributes to three scaled classes using 1-D SOM, as well as low, middle and high). The applied error measure for measure of performance of 2 ( )

## 1

m real classified d d i i i MSE m
∑ −
= = ; 

## Rst Is Given By:

In deducing of decision for approximated rules (not unique decision part), we select highest value (largest ambiguities) for such decisions. By repeating of steps as well pervious situation, we obtain other behavior of SORST where we employγ =1, in equation 4(fig.9&fig.10). Fig.9 shows how with keeping of β as a fixed value, N.G 
after α =.8 gets in to the steeper rate while it has endured relatively high transition time. In fact government without changing of his affecting power ( β ) preserve, in a long time society between "tranquil and rushing". Other guessed difference with first option, is on the fast change of the society over passing time and for high α values as though MSE exhibits a low range of variations. 