using the fuzzy self-organization algorithm. This 
algorithm splits the data space into a specified 
number of overlapping clusters. Each cluster may 
be associated with the specific rule of the center 
corresponding to the center of the appropriate 
cluster. In this way all rules correspond to the regions of the space-containing majority of data and the problem of the empty rules can be avoided. The 
ultimate goal of data clustering is to partition the 
data into similar subgroups. This is accomplished 
by employing some similar measures (e.g., the Euclidean distance) [9]. In this paper data clustering is used to derive 
membership functions from measured data, which, 
in turn, determine the number of If-Then rules in the 
model (i.e., rules indication). The method employed in this paper is the 
subtractive clustering method, proposed by Yager as one of the simplest clustering methods [10]. 2.3. *Rough Set Theory (RST)* The rough set theory introduced by Pawlak [4], [5] 
has often proved to be an excellent mathematical 
tool for the analysis of a vague description of 
object. The adjective vague referring to the quality of information means inconsistency, or ambiguity which follows from information granulation. 
An information system is a pair *S=< U, A >*, where 
U is a nonempty finite set called the universe and A is a nonempty finite set of attributes. An attribute a can be regarded as a function from the domain U to 
some value setVa . An information system can be 
represented as an attribute-value table, in which rows are labeled by objects of the universe and 
columns by attributes. With every subset of 
attributes B⊆ A, one can easily associate an 
equivalence relation B I on U: 
$I_{B}=\{(x,y)\in U:for\ every\ a\in B,a(x)=a(y)\}$ (1)  Then, $I_{B}=\bigcap_{a\in B}I_{a}$.  If $X\subseteq U$,the sets $\{x\in U\{x\}_{B}\subseteq X\ \}$ and $\{x\in U:\left[x\right]_{B}\cap X\neq\varphi\}$, where $\left[x\right]_{B}$denotes the $\{x\in U:\left[x\right]_{B}\cap X\neq\varphi\}$, where $\left[x\right]_{B}$ denotes the $\{x\in U:\left[x\right]_{B}\cap X\neq\varphi\}$, where $\left[x\right]_{B}$ denotes the $\{x\in U:\left[x\right]_{B}\cap X\neq\varphi\}$.  
equivalence class of the object x U∈ relative to B I , are called the B-lower and the B-upper approximation of X in S and denoted by BX and BX , respectively. Consider 12 n U = {x , x , ...,x } and 12 n A = {a , a , ...,a } in the information systemS= U, A p f . 

 By the discernibility matrix *M(S)* of S is meant an n*n matrix such that 

$$)\neq a(x_{j})\Big\}$$

c a A ax ax *ij i j* =∈ ≠ { :( ) ( )} (2) 
A discernibilty function s f is a function of m Boolean variables 1... m a a corresponding to attribute 1... m a a , respectively, and defined as follows: 

1 ( ,..., ) { ( ) : , , , } s m ij ij f *a a c i j n j ic* = ∧∨ ≤ ≠ p ϕ (3) 
Where ( ) ij ∨ c is the disjunction of all variables 
with ij a c ∈ .Using such discriminant matrix the 
appropriate rules are elicited. One of the main parameters in the covering of the obtained rule is "dependency rule or strength ". Let; 
we have a rule in the disjunctive normal form 
(D.N.F), for instance: 
1 1 ( ... ) ( ... )..... ( )
The dependency factor $df_{i}$ is given by:  $$df_{i}=\frac{Card\left(POS_{B_{i}}\left(d_{i}\right)\right)}{Card\left(U_{i}\right)}\tag{4}$$  Where $POS_{B_{i}}\left(d_{i}\right)=\bigcup_{X\in I_{d_{i}}}B_{i}\left(X\right),$and $B_{i}\left(X\right)$is the lower approximation of $X$ with respect 
n mi
a a b a d decision attribute ∧ ∨ ∧∧ → **1444442444443**

the lower approximation of X with respect to ( ) Bi X . Bi is the set of condition attributes( say inputs) occurring in the rule. ( ) Bi *POS di* is the positive region of the decision class i d that can be surely described by attributes Bi [6]. The existing induction algorithms use one of the following strategies: (a) Generation of a minimal set of rules covering all objects from a decision table; 
(b) Generation of an exhaustive set of rules consisting of all possible rules for a decision table; (c) Generation of a set of `strong' decision rules, even partly discriminant, covering relatively many objects each but not necessarily all objects from the decision table [11]. In this study we have developed RST in MatLab7, and on this added toolbox other appropriate algorithms have been prepared. 

## 3. Proposed Algorithms

In the whole of our algorithms, we use four basic axioms upon the balancing of the successive granules: 
Step (1): dividing the monitored data into groups of training and testing data Step (2): first granulation (crisp) by SOM or other crisp granulation methods Step (2-1): selecting the level of granularity 