In this study the possibility of using Fuzzy Inference system efficiency, creating a bridge between meteorological, plant parameters, and Daily Yield, and comparing the accuracy of Daily Yield using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, seven models for estimating daily Yield were presented. For these models, the calculated Yield from AQUACROP model was considered as a base and the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination, Jacovides (t) and Sabbagh et al. (R2/t) criteria. An experiment was carried out during the 2014-2015 growing season in the Agricultural Research and Education Center of Khorasane Razavi province using a randomized complete block design with a split plot arrangement and four replications. This experiment was including of three irrigation levels treatments as the main plot and three method of planting treatments (transplanting 20-days, transplanting 30-days and direct seeded) as subplots. From the available data, 75 percent was used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the Fuzzy models with different input parameters as compared with AQUACROP model showed that Fuzzy systems were very well able to estimate the daily Yield. Fuzzy model so that the highest correlation with the 9 input variables (r=0. 98) had in mind and evaluate other parameters, the model with 2 parameters, match very well with the AQUACROP model had stage training. In the test phase, training phase was very similar results and the model with the second phase of harvest index and canopy cover will get the best match. According to the results of this study it can be concluded that Fuzzy model approach is an appropriate method to estimate the daily yield.

To estimate actual evapotranspiration of grass, an experiment was a weather stationat, the Faculty of Agriculture, Ferdowsi University of Mashhad in 1389 year. In this experiment, actual evapotranspiration grass Deficit irrigation at different levels (5 levels) with a single branch sprinkler system, at two-day period was measure the water balance method. Also was estimated the reference crop evapotranspiration with FAO Penman, Hargreaves-Samani and pan evaporation methods. Coefficients calculated for each plant with water level and Five Fuzzy model was provided for estimating actual daily evapotranspiration. In these models was considered FAO Penman evapotranspiration as the output model. Performance models were compared using RMES, MAE, MBE, t and R2/t. The results showed that evapotranspiration values calculated in terms of standard methods of FAO Penman and Hargreaves - Samani, compared with the water balance method, respectively, 17 and 14 percent more than had been estimated. With analysis of evapotranspiration values in non-standard conditions were found to reduce grass Deficit irrigation is the actual evapotranspiration, The difference amounts to 20 percent evapotranspiration Deficit irrigation conditions was not a significant effect on evapotranspiration. Fuzzy model output results also showed that the Fuzzy models developed using the combined model (PMF56) had a high Match and the ability to estimate actual evapotranspiration are included in the daily scale.

In recent years, using Fuzzy sets theory in modeling of complex and uncertain hydrological phenomena has attracted research workers. For this reason, in this research for river flow forecasting, we have used models of FIS and ANFIS which are based on Fuzzy logic. Data of daily flow discharges were provided from Lighvanchay watershed for 6 years. For considering the randomness of data, return points test was used. Then correlogram of data was employed to determine the input optimum models and finally 5 models of discharge forecasting designed based on previous days' discharge. The results showed that ANFIS was more precise and less disperse (RMSE=0.0234) with compare to FIS (RMSE=0.1982). The ANFIS was also more precise in peak discharges simulation than FIS.

Suppliers are one of the most vital parts of supply chain whose operation has significant indirect effect on customer satisfaction. Since customer's expectations from organization are different, organizations should consider different standards, respectively. There are many researches in this field using different standards and methods in recent years. The purpose of this study is to propose an approach for choosing a supplier in a food manufacturing company considering cost, quality, service, type of relationship and structure standards of the supplier organization. To evaluate supplier according to the above standards, the Fuzzy Inference system has been used. Input data of this system includes supplier's score in any standard that is achieved by AHP approach and the output is final score of each supplier. Finally, a supplier has been selected that although is not the best in price and quality, has achieved good score in all of the standards.

A Fuzzy Inference system is a mapping of input-to-output space implemented using membership functions and Fuzzy rules. Intelligent, control and decision making is one of the most important Fuzzy Inference algorithms. Mamdani and Sugno Fuzzy Inference algorithms are examined and their advantages and disadvantages are stated.

The Fuzzy Sets Theory has recently been widely and successfully used in engineering problems with complexity, ambiguity, or lack of enough data. The Fuzzy Inference System (PIS) is among these techniques. The main advantage of this technique over traditional methods is that it works based on IF-THEN rules and appoints the relation between input and output variables accordingly. In this study the monthly discharge, temperature, and rainfall are used in the Fuzzy Inference System context as continuous series in order to forecast the river flow discharge for the next months. The effect of each variable in previous time step is determined on the flow discharge in the upcoming month and the best combination and suitable lag time is obtained.

Sharp and broad crested weirs are the most commonly used tools for measuring the flow in irrigation and drainage networks. Considering the importance of exact calculation of discharge and the just allocation of water, estimating the discharge coefficient for the weirs is so essential and important. In this research, application and reliability of two intelligent models of Fuzzy Inference system and ANFIS models were studied for estimation of the rectangular sharp-crested and broad crest weir discharge coefficient. For this purpose, a laboratory flume was used to determine the flow velocity and flow rate over different weirs. Then, analytical evaluations were made using optimization models for determination of weirs’ discharge coefficients. Statistical analysis of the results showed that ANFIS model with the least amount of RMSE and R2=0.96 for sharp-crested and R2=0.95 for broad-crested weirs is the most reliable method in comparison to the others.