ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM ((ANFIS)) and genetic algorithm-artificial neural network (GA-ANN) were used for modeling of the hot-air drying kinetics of papaw slices. The (ANFIS) and GA-ANN were fed with 3 inputs of drying time (0-320 min), drying temperature (40, 50 and 60 °C) and slice thickness (3, 5 and 7 mm) for prediction of moisture ratio (MR). The triangular membership functions (MFs) were applied and 27 rules were provided for the (ANFIS) designing. The developed (ANFIS) predictions were relatively similar to the experimental data (R2=0.9967 and RMSE=0.0161). The optimized GA-ANN, which included 7 hidden neurons, predicted the MR with a good precision (R2=0.9936 and RMSE=0.0220). The effective diffusivity for papaw slices was within the range of 6.93 ×10-10 to 1.50×10-9 m2/s over the temperature range. The activation energy was found to be 32.5 kJ/mol indicating the effect of temperature on diffusivity.

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.

One of the most significant threats of a national economy is the bankruptcy of its firms. Assessment of bankruptcy provides valuable information on which governments, investors and shareholders can base their financial decisions in order to prevent possible losses. The aim of this study was to model bankruptcy by using ADAPTIVE Neuro Fuzzy INFERENCE SYSTEM ((ANFIS)). Statistical society for performing of this research is companies which were listed at Tehran Stock Exchange since 2001 up to 2010 and according to article 141 of commercial code, including 40 bankrupt companies and 40 non bankrupt companies. These companies were divided randomly in three sets: train set for creating model, test set and check set for validating model. financial ratios of the companies in the year before bankruptcy were considered as input variables (ANFIS). The result of this study points out that percentage of success predictions one year before bankruptcy is 83.75. Finally, according to this study, the (ANFIS) selection is helpful to predict the financial distress situation for companies which were listed at Tehran Stock Exchange.

Sugarcane fields are affected by different parameters and factors such as ground water table, salinity of saturated soil, depth of irrigation, variety and age of plants and etc. Evaluating effects of these parameters, it is possible to propose solutions to maximize sugarcane fields performance. In this paper ADAPTIVE Neuro - Fuzzy INFERENCE SYSTEM ((ANFIS)) is used to model the performance of sugarcane fields. This study is performed based on three years data of "Mirza koochak khan cultivation and industry". Results showed that the proposed model has a correlation factor of 0.978, RMSE of 1.35 and error of 3.2 The proposed model has a very high accuracy in performance forecasting of sugarcane fields.

Limitations on freshwater resources have caused researchers and water resources managers to focus an increasing attention over the past few decades on water quality protection. Surface water quality management in such resources as rivers, seas, lakes, and estuaries is of a greater importance than other water resources and a greater number of studies have been conducted on them as they are more accessible and, therefore, more directly exposed to a variety of contaminants and pollutants. Application of appropriate and efficient mathematical models for river water quality simulation is essential for the formulation of comprehensive guidelines used in evaluating measures that are employed for river pollution control and management. The non-linear equations dominating pollutant transfer phenomena in rivers, the complexity of their simultaneous solution, and the multiplicity of kinetic constants and coefficients have made it difficult, or at times impossible, to use physically-based models and methods for this purpose. Therefore, most of these models can only be applied to simplified cases or to situations where the models are strictly calibrated and validated, with no adequate accuracy when applied to unrestricted conditions. The uncertainties in water quality problems have made fuzzy INFERENCE SYSTEMs, especially as combined with ADAPTIVE neural networks, to be used as a novel approach. The main objective of the present study is to exploit the capabilities of the ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM ((ANFIS)) for river quality predictions with emphasis on DO and BOD. In the case study carried out on the Zayandehroud River, BOD predictions were obtained by the proposed SYSTEM with a correlation coefficient of 0.953 in the calibration stage and 0.931 in the validation stage and DO predictions were obtained with a correlation coefficient of 0.921 in the calibration stage and 0.904 in the validation stage. Comparison of the results provided by the ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM and the measured values reveals the high accuracy level of the proposed model.

This paper investigates the predictability of stock market index using an ADAPTIVE Network-Based Fuzzy INFERENCE SYSTEM ((ANFIS)). The goal is to determine whether an (ANFIS) algorithm is capable to predict stock market return and trying to find the best architecture. We attempt to model and predict the return on stock price index of the Tehran Stock Exchange (TEDPIX) with (ANFIS). We use six macroeconomic variables as input variables. The experimental results reveal that the model successfully forecasts the daily return on TEDPIX Index. (ANFIS) can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.

The most current way for measuring the soil fragmentation is determination of mean weight diameter (MWD). In this study, the ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM ((ANFIS)) was used to predict of range soil fragmentation affected by different grazing intensities, distance from village and sampling depth. Present study conducted at 2015 in 3 adjacent rural areas (Alvars, Aldashin and Asbe marz) in Darvishchai watershed in Ardabil County. The studied parameters on the soil fragmentation including different grazing intensities in 3 levels (low, medium and high intensity), distance from village in 3 levels (200, 400 and 600 meters) and the soil sampling depths in 2 levels (0-15cm and 15-30cm). Obtained data were transferred to MATLAB software for the development of (ANFIS) models. For evaluating the models operation, mean squares error (MSE) and correlation (R2) were used. The result of best (ANFIS) model in prediction of soil fragmentation was compared with results of regression model. The results show that different grazing intensities, distance from village, sampling depth and their combinations had significant effect on the soil fragmentation. Increase of grazing intensity resulted in increment of soil fragmentation. With increment the distance from village from 200 to 400 meters, soil fragmentation decreased but with increment of distance, increased. Soil fragmentation in all conditions was higher at depth of 0-15 cm than depth of 15-30 cm. (ANFIS) model had more precision in prediction of soil fragmentation (R2=0. 96) relative to regression model (R2=0. 76).