scholarly journals Hybrid Model of Singular Value Decomposition, ANFIS and Genetic Algorithm for Prediction of Sediment Transport in Sewers

2020 ◽  
Author(s):  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Amir Mosavi ◽  
Seyed Hamed Ashraf Talesh ◽  
Ali Jamali ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Size ◽  
Singular Value Decomposition ◽  
Fuzzy Inference ◽  
Sediment Concentration ◽  
Singular Value ◽  
Inference System ◽  
Pareto Curve ◽  
Median Diameter ◽  
Value Decomposition

Densimetric Froude (Fr) is the minimum velocity required to prevent sediment deposition in pipes. Prediction of Fr is of utmost importance in numerous applications in civil engineering. In this paper through using a new hybrid method. Genetic Algorithm (GA) is used for optimum selection of membership functions of Adaptive Neuro-Fuzzy Inference System (ANFIS), and Singular Value Decomposition (SVD) method is used to compute the linear parameters of ANFIS’s results section (ANFIS-GA/SVD). Also, two different target functions are known as training error (TE) and prediction error (PE) by Pareto curve, the trade-off between these functions is selected as the optimal modeling point. First, different models will be presented using the parameters affecting Fr prediction, classifying them in different groups; then the Fr parameter will be predicted for all the different models through utilizing three different sets of data and the ANFIS-GA/SVD technique. The results of the models indicate that the best Fr prediction is obtained when independent parameters such as volumetric sediment concentration (CV), ratio of median diameter of particle size to pipe diameter (d/D), ratio of median diameter of particle size to hydraulic radius (d/R) and overall friction factor of sediment (λs) use as input variables in prediction of Fr. A sensitivity analysis is also conducted for the purpose of examining the effect of each of the dimensionless parameters on Fr prediction accuracy. Comparing the results of the suggested models with the existing regression-based equations shows that ANFIS-GA/SVD (R2=0.986, MAPE=4.397, RMSE=0.206, SI=0.053, ρ=0.026, BIAS=-0.025) is more accurate than the rest of the models.

scholarly journals Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098056
Author(s):  
Walid Touzout ◽  
Djamel Benazzouz ◽  
Fawzi Gougam ◽  
Adel Afia ◽  
Chemseddine Rahmoune
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Vibration Signal ◽  
Relevant Information ◽  
Singular Value ◽  
Inference System ◽  
Operation Conditions ◽  
Neuro Fuzzy ◽  
Value Decomposition

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

scholarly journals Modeling Electromechanical Overcurrent Relays Using Singular Value Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Feng-Jih Wu ◽  
Chih-Ju Chou ◽  
Ying Lu ◽  
Jarm-Long Chung
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference ◽  
Singular Value ◽  
Time Characteristic ◽  
Characteristic Curves ◽  
Inference System ◽  
Novel Approach ◽  
Curve Fit ◽  
Overcurrent Relays ◽  
Value Decomposition

This paper presents a practical and effective novel approach to curve fit electromechanical (EM) overcurrent (OC) relay characteristics. Based on singular value decomposition (SVD), the curves are fitted with equation in state space under modal coordinates. The relationships between transfer function and Markov parameters are adopted in this research to represent the characteristic curves of EM OC relays. This study applies the proposed method to two EM OC relays: the GE IAC51 relay with moderately inverse-time characteristic and the ABB CO-8 relay with inverse-time characteristic. The maximum absolute values of errors of hundreds of sample points taken from four time dial settings (TDS) for each relay between the actual characteristic curves and the corresponding values from the curve-fitting equations are within the range of 10 milliseconds. Finally, this study compares the SVD with the adaptive network and fuzzy inference system (ANFIS) to demonstrate its accuracy and identification robustness.

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