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Improved Side Weir Discharge Coefficient Modeling by Adaptive Neuro-fuzzy Methodology
Shahaboddin Shamshirband,Hossein Bonakdari,Amir Hossein Zaji,Dalibor Petkovic,Shervin Motamedi 대한토목학회 2016 KSCE JOURNAL OF CIVIL ENGINEERING Vol.20 No.7
In this article, the accuracy of a soft computing technique is evaluated in terms of discharge coefficient prediction of an improved triangular side weir. The process includes simulating the discharge coefficient with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Matlab software is used for ANFIS modeling. To identify the most appropriate input variables, eight different input combinations with various numbers of inputs are examined. The performance of the proposed system is confirmed by comparing the ANFIS and experimental results for the testing dataset. The performance evaluation demonstrates that the ANFIS model with five inputs (Root Mean Square Error (RMSE) of 0.014) is more accurate than the ANFIS model with one input (RMSE = 0.088). The ANFIS model results are also compared with the results obtained from previous regression and soft computing studies.
Shamshirband, Shahaboddin,Keivani, Afram,Mohammadi, Kasra,Lee, Malrey,Hamid, Siti Hafizah Abd,Petkovic, Dalibor Elsevier 2016 RENEWABLE & SUSTAINABLE ENERGY REVIEWS Vol.59 No.-
<P><B>Abstract</B></P> <P>The prime aim of this study is appraising the suitability of adaptive neuro-fuzzy inference framework (ANFIS) to compute the monthly wind power density. On this account, the extracted wind power from Weibull functions are utilized for training and testing the developed ANFIS model. The proficiency of the ANFIS model is certified by providing thorough statistical comparisons with artificial neural network (ANN) and genetic programming (GP) techniques. The computed wind power by all models are compared with those obtained using measured data. The study results clearly indicate that the proposed ANFIS model enjoys high capability and reliability to estimate wind power density so that it presents high superiority over the developed ANN and GP models. Based upon relative percentage error (RPE) values, all estimated wind power values via ANFIS model are within the acceptable range of −10% to 10%. Additionally, relative root mean square error (RRMSE) analysis shows that ANFIS model has an excellent performance for estimation of wind power density.</P>