Developed Adaptive Neuro-fuzzy Inference System Models Research Articles

Forecasting of Flood Flow of Panam River Basin using Adaptive Neuro-Fuzzy Inference System (ANFIS) and ANN with Comparative Study

Flood forecasting is one of the most important issues in the hydrology due to its indispensable contribution in lowering monetary and life losses. In recent years reliability of flood forecasting using various modelling tools has improved to a great extent due to hydrologic modelling, development in expertise and knowledge, and advancement in the collection of data and algorithms for evaluation. The current study presents the use of the Adaptive Neuro Fussy Inference System (ANFIS) in forecasting floods for the Panam River basin system. ANFIS combines neural network algorithms and fuzzy reasoning to map an input space to an output space. This paper includes the development of ANFIS models using various membership function and their comparison for forecasting the inflow rate into the Panam dam, which creates flooding conditions on the downstream side. The different evaluation parameters like Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R2) and Discrepancy Ratio (D) are used to evaluate the results of each model. From all the developed ANFIS models, the best ANFIS model is selected, having RMSE as 271.91, R as 0.98, R2 as 0.96, and D as 1.00 for training the model, and RMSE as 2000.74, R as 0.95, R2 as 0.90 and D as 1.12 for validating the model. Artificial Neural Network (ANN) model has also been developed for forecasting flood. In the Neural network there are total 3 types of transfer function in each layer i.e., LOGSIG, TANSIG and PURELIN. All the developed is evaluated with the coefficient of correlation (R) and Mean Squared Error (MSE). In the end, future headings for innovative research work are discussed.

Modeling of Transverse Momentum Spectra for Charged Particles in Proton–Proton Collisions Based on Soft Computing Approaches

ABSTRACTThe transverse momentum (pT) distributions (or spectra) of charged particles produced in high and ultra-high energy proton–proton (pp) collisions are studied by the neuro-fuzzy model. The objective of the present work is developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for calculating and predicting the transverse momentum spectra of charged particles as a function of pT and the center-of-mass energy (), as well as the modeling of the average transverse momentum ⟨pT⟩ as a function of , i.e., we have proposed and developed two models. The ANFIS models were designed based on available experimental data for = 53 GeV, 200 GeV, 546 GeV, 900 GeV, 1800 GeV, 2360 GeV and 7 TeV. The empirical results from the developed ANFIS models for pT distributions, as well as ⟨pT⟩ for pp collisions are compared with the theoretical ones which are obtained from other models. The comparison results show a great deal of agreement between the available experimental data (up to 7 TeV) and the theoretical ones. At full large hadron collider (LHC) energy ( = 14 TeV), we have predicted the pT spectra and ⟨pT⟩ which also, show a good agreement with different models.

Prediction and optimization of hydrogen yield and energy conversion efficiency in a non-catalytic filtration combustion reactor for jet A and butanol fuels

Hydrogen production is one of main subjects in fuel cells. The traditional method of synthesis gas production is based on fuel reforming using catalysts. The main problem of these methods is sensitivity and fast degradation of catalysts especially when fuels with high sulfur content are used. A new technique for hydrogen production is fuel-reforming using non-catalytic filtration combustion in porous media reactors. Various experimental works have been carried out to increase hydrogen production under different operating conditions such as inlet fuel velocity and equivalence ratio. First, we investigated the ability of adaptive neuro fuzzy inference system (ANFIS) for predicting the filtration combustion characteristics. Four distinct ANFIS models were developed for estimating the hydrogen yield and energy conversion efficiency for fuels of jet A and butanol. Eight different membership functions of dsigmf, gauss2mf, gaussmf, gbellmf, pimf, psigmf, trapmf and trimf were tested for training of the ANFIS networks. The results showed that the RMSE of the best developed ANFIS models for estimating of the hydrogen yield of jet fuel, hydrogen yield of butanol, conversion efficiency of jet fuel and conversion efficiency of butanol were 1.399, 1.213, 0.508 and 2.191, respectively. Moreover the R2 values of 0.998, 0.998, 0.999 and 0.999 were obtained for predicting the above mentioned variables, respectively. In the second step, a novel algorithm based on imperialist competitive algorithm (ICA) was used for optimization of hydrogen yield and energy efficiency. The maximum value of hydrogen yield and energy efficiency was 50.46% and 67.88% for jet A and 47.27% and 96.93% for butanol, respectively. The results showed that the imperialist competitive algorithm is an efficient and powerful algorithm to optimize combustion processes.

Parametric optimization of friction stir welding process of age hardenable aluminum alloys−ANFIS modeling

A comparative approach was performed between the response surface method (RSM) and the adaptive neuro-fuzzy inference system (ANFIS) to enhance the tensile properties, including the ultimate tensile strength and the tensile elongation, of friction stir welded age hardenable AA6061 and AA2024 aluminum alloys. The effects of the welding parameters, namely the tool rotational speed, welding speed, axial load and pin profile, on the ultimate tensile strength and the tensile elongation were analyzed using a three-level, four-factor Box-Behnken experimental design. The developed design was utilized to train the ANFIS models. The predictive capabilities of RSM and ANFIS were compared based on the root mean square error, the mean absolute error, and the correlation coefficient based on the obtained data set. The results demonstrate that the developed ANFIS models are more effective than the RSM model.