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.

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.

fuzzy Mathematics theory has been studied extensively in this study. Most early interest in fuzzy set theory pertained to representing uncertainty in human cognitive processes. Fuzziness can be found in many areas of daily life, such as engineering, business, medical and related health sciences, and the natural sciences. It is particularly frequent, however, in all areas in which human judgment, evaluation and decisions are important. However, in many real-life cases, the decision data of human judgments with preferences are often vague, so the traditional ways of using crisp values are inadequate. Due to a lack of information, the future state of the real system might not be known completely. fuzzy mathematical modelling represents a real-world problem in a mathematical form to find solutions to the problem that correspond closely to how humans perceive it. Also, fuzzy modelling increases the validity of ill-structured systems by offering a deeper understanding of the complexities of decision parameters. In this paper, we analyze the problem of the transgender using FCMs and Combined fuzzy Cognitive Maps (CFCM). This paper is divided into five sections. Section one introduces the basic Introduction of transgender and the previous research of fuzzy Cognitive Maps (FCM) and Combined FCM. Section two introduces FCM and combined FCM and the basic operations. Section three shows the basic definitions of FCM and Combined CFCM. The fourth section provides methods to analyze transgender problems using combined FCM. The final section gives the conclusions and some suggestions based on our study.

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

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.

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.

The main purpose of this study is to develop a fuzzy inference system to predict bank profitability in Iran and help investors in their investment decisions. For this purpose, the main effective variables on bank profitability, including facilities, deposits, manpower costs, and assets were recognized. In the next step, the data of 13 banks were collected from 2001 to 2011. The membership functions and fuzzy rules were developed in the MATLAB software and then, fuzzy inference system was developed. The findings revealed that the system has an appropriate performance in predicting profitability of Iranian banks and rarely makes any error in this area. The predicted profitability of many banks has increased during the study period and also the predicted profitability of private banks was more than public banks. The banks of Industry and Mine and Karafarin Bank had the least profitability and Mellat Bank had the highest. Finally, Post Bank had the most errors while Mellat Bank had the fewest errors.