Energy is regarded as one of the most important elements in agricultural sector. During the last decades energy consumption in agriculture has increased, so finding the relationship between energy consumption and crop yields in agricultural production can help to achieve sustainable agriculture. In this study several adaptive neuro-fuzzy inference system (ANFIS) models were evaluated to predict...

The main purpose of the current research is comparing the results of Artificial Neural Network (ANN) with Adaptive Neuro-Fuzzy Inference System (ANFIS) with regard to determination of the importance of soil properties affecting clay dispersibility. After taking samples from two depths of 0-40 and 40-80 cm, the spontaneous and mechanical dispersions of clay were recorded using both weighing and ...

Reinforced concrete beam; Shear strength; Artificial neural network; Adaptive neuro-fuzzy inference system; Iranian concrete institute code; American concrete institute code. Abstract In this paper, the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are used to predict the shear strength of Reinforced Concrete (RC) beams, and the models are compared with A...

-The accurate prediction of student academic performance is of importance to institutions as it provides valuable information for decision making in the admission process and enhances educational services by allocating customized assistance according to the predictions. The purpose of this study is to investigate the predictive ability of two models: the hierarchical ANFIS and ANN. We used prev...

Power demand forecasting is a significant factor in the planning and economic and secure operation of modern power system. This research work has compared different forecasting techniques and opted to find out better technique in context of power generation, which varies rapidly from time to time. The dataset has been generated from yearly demand of electricity of Bangladesh for last five years...

In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design an Inverse Kinematic based controller forthe inverse kinematical control of SCORBOT-ER V Plus. The proposed ANFIS controller combines the advantages of a fuzzy controller as well as the quick response and adaptability nature of an Artificial Neural Network (AN...

This study investigates the prediction model of compressive strength of self–compacting concrete (SCC) by utilizing soft computing techniques. The techniques consist of adaptive neuro–based fuzzy inference system (ANFIS), artificial neural network (ANN) and the hybrid of particle swarm optimization with passive congregation (PSOPC) and ANFIS called PSOPC–ANFIS. Their perf...

This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. T...

In this paper river stage discharge models using Adaptive NeuroFuzzy Inference System (ANFIS) and Linear Multiple Regression (MLR) methods have been developed. This paper also investigates the best model to forecast river discharge. From the literature it is clear that ANN models and Fuzzy logic models are quite applicable on river stage discharge modelling. Hence this present study carried out...

ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with oth...