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2.2. *Neuro-fuzzy inference system ( NFIS)*
There are different solutions of fuzzy inference systems. Two well-known fuzzy modeling methods are the Tsukamoto fuzzy model and Takagi– 
Sugeno–Kang (TSK) model. In the present work, only the TSK model has been considered. The TSK fuzzy inference systems can be easily implanted in the form of a so called Neuro-fuzzy network structure .in this study, we have employed an adaptive neuro-fuzzy inference system [9]. 

One of the most important stages of the Neurofuzzy TSK network generation is the establishment of the inference rules. Often used is 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 ni 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 