The paper presents an adaptive neuro-fuzzy inference system (ANFIS) based modeling approach to predict the monthly global solar radiation (MGSR) in Bhubaneswar. Comparisons of the predicted and measured value of monthly global solar radiation (GSR) on a horizontal surface are presented. The input parameters of the model used in this paper are sunshine duration, temperature, humidity, clearness ...

Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ...

In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated ...

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and...

Most of the earlier studies in the inventory control and management make assumption that the manufacturing system is reliable and does not fail. However, in the real industrial applications, there is no completely reliable manufacturing system; Machine failure occur and the production does not resume before repair. In this paper we will study and analyze the optimal lot size in a real productio...

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input-output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input-output data pairs. Good...

This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer...

The late detection of the kick (the entrance of underground fluids into oil wells) leads to oil well blowouts. It causes human life loss and imposes a great deal of expenses on the petroleum industry. This paper presents the application of adaptive neuro-fuzzy inference system designed for an earlier kick detection using measurable drilling parameters. In order to generate the initial fuzzy inf...

In this paper we apply an Adaptive Network-Based Fuzzy Inference System (ANFIS) with one input, the dependent variable with one lag, for the forecasting of four macroeconomic variables of US economy, the Gross Domestic Product, the inflation rate, six monthly treasury bills interest rates and unemployment rate. We compare the forecasting performance of ANFIS with those of the widely used linear...

314 Abstract— Neuro-Fuzzy systems are hybrid intelligent systems which combine features of both paradigmsfuzzy logic and artificial neural networks. Adaptive Neuro Fuzzy Inference System (ANFIS) is one of such architecture which is widely used as solution for various real world problems. This paper describes development of an ANFIS model for FPGA implementation. Model can be realized with hardw...