One of the most important stages of the Neuro-fuzzy TSK network generation is the establishment of the inference rules. Often employed method is used the so-called grid method, in which the rules are defined as the combinations of the membership functions for each input variable. 

If we split the input variable range into a limited number (say i n for *i=1, 2... n*) of membership functions, the combinations of them lead to many different inference rules. The problem is that these combinations correspond in many cases to the regions of no data, and hence a lot of them may be deleted. This problem can be solved by using the fuzzy selforganization 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-(Nauk et al, 1997). 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 Chiu as one of the simplest clustering methods (Chiu, 1994). 

## 2.3 Self-Organizing Feature Map (Som)

Kohonen's SOM algorithm has been well renowned as an ideal candidate for classifying input data in an unsupervised learning way. Kohonen self-organizing networks (Kohonen feature maps or topology-preserving maps) are competition-based network paradigm for data clustering. The learning procedure of Kohonen feature maps is similar to the competitive learning networks. The main idea behind competitive learning is simple; the winner takes all. The competitive transfer function returns neural outputs of 0 for all neurons except for the winner which receives the highest net input with output 1. SOM changes all weight vectors of neurons in the near vicinity of the winner neuron towards the input vector. Due to this property SOM, are used to reduce the dimensionality of complex data (data clustering). Competitive layers will automatically learn to classify input vectors, the classes that the competitive layer finds are depend only on the distances between input vectors (Kohonen, 1986). 

## 2.4 A Combining Of Som&Nfis: Sonfis

In this part, we reproduce the proposed a hybrid intelligent algorithm in (Owladeghaffari et al, 2008): Step (1): dividing the monitored data into groups of training and testing data Step (2): first granulation (clustering) by SOM or other crisp granulation methods Step (2-1): selecting the level of granularity randomly or depend on the obtained error from the NFIS or RST (regular neuron growth) Step (2-2): construction of the granules (no-fuzzy clusters). 

Step (3): second granulation by NFIS or RST 
Step (3-1): crisp granules as a new data. 

Step (3-2): selecting the level of granularity; (Error level, number of rules, strength threshold...) Step (3-3): checking the suitability. (Close-open iteration: referring to the real data and reinspect closed world) 
Step (3-4): construction of fuzzy/rough granules. 

Step (4): extraction of knowledge rules Balancing assumption is satisfied by the close-open iterations: this process is a guideline to balancing of crisp and sub fuzzy/rough granules by some random/regular selection of initial granules or other optimal structures and increment of supporting rules (fuzzy partitions or increasing of lower /upper approximations ), gradually. In this study, we use only two known intelligent systems: SOM&NFIS. The overall schematic of Self Organizing Neuro-Fuzzy Inference System -Random -: SONFIS-R has been shown in figure1. 