The results of first granulation by 17*1 neurons in competitive layer of SOM has been portrayed in figure 7, as matrix plot form.  It must be notice here; we reduced all of objects in to the 17 patterns, which are in balance with the simplest rules of NFIS, while we had employed error measure criteria to balancing. SONFIS-R which has been employed in other comprehensive data set, show ability of this system in detection of the dominant structures on the attributes and representation of the simplest rules, as well as one wishes to catch up (Owladeghaffari et al,2008).

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

Table2.Extrated rules by NFIS in SONFIS-R ( in i ( i=1:12 ) is agree with the decision table attributes and mf j(j=1,2) is proper with blue and green curves, respectively. 1. If (in1 is in1mf1) and (in2 is in2mf1) and (in3 is in3mf1) and (in4 is in4mf1) and (in5 is in5mf1) and
(in6 is in6mf1) and (in7 is in7mf1) and (in8 is in8mf1) and (in9 is in9mf1) and (in10 is in10mf1) and
(in11 is in11mfl) and (in12 is in12mfl) then (fl)
2. If (in1 is in1mf2) and (in2 is in2mf2) and (in3 is in3mf2) and (in4 is in4mf2) and (in5 is in5mf2) and
(in6 is in6mf2) and (in7 is in7mf2) and (in8 is in8mf2) and (in9 is in9mf2) and (in10 is in10mf2) and
(in11 is in11mf2) and (in12 is in12mf2) then (f2)
It is noteworthy that the density of membership functions (fig8) and the histogram of variables appeared by SOM (fig7) have been coincided. Add to this in second granulation level -Fuzzy rules- decision part of rules are in the linear (one order) form of premises ( f1 &f2 in table2). In a supplementary way to take visual relates on the most effective parameters based on the reduct members, we use the fuzzy rules on those attributes and decision part (fig 9). Figure 9 shows how dilution changes under the main effective parameters, produced by RST as a reduct