scholarly journals Predicting the Performance of Passive Solar Distillation Using Generalized Regression Neural Network

2021 ◽  
Author(s):  
Siva Sankaran
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Performance Appraisal ◽  
Network Architecture ◽  
Performance Estimation ◽  
Generalized Regression Neural Network ◽  
Atmospheric Conditions ◽  
Solar Distillation ◽  
Generalized Regression ◽  
Passive Solar

Abstract In this study, the performance appraisal of the passive double slope solar distillation (PDSSD) was predicted using the generalized regression neural network (GRNN) model. The performance estimation of passive solar distillation is a complicated one because of unsteady and uncertain atmospheric conditions. For this purpose, a set of experiments has conducted for seven successive days, and results were compared with the GRNN model. The proposed GRNN consists of five inputs (solar irradiance, ambient temperature, basin temperature, surface water temperature, glass cover temperature) and two outputs (distillate yield and efficiency). Such network architecture was trained and validated with a set of experimental data values. The predicted results of the GRNN model follow a good trend with experimental data. The overall accuracy of the predicted GRNN is 99.58%.

Application of Generalized Regression Neural Network in Predicting the Performance of Natural Convection Solar Dryer

2019 ◽  
Vol 142 (3) ◽  
Author(s):  
M. Sridharan
Keyword(s):  
Neural Network ◽  
Natural Convection ◽  
Network Architecture ◽  
Experimental Studies ◽  
Generalized Regression Neural Network ◽  
Atmospheric Conditions ◽  
Time Duration ◽  
Transient Nature ◽  
Solar Dryer ◽  
Generalized Regression

Abstract The performance evaluation of a natural convection solar dryer is a complex one because of the transient and non-linear nature of atmospheric conditions. In this comparative study, a smart neural network -based tool was developed for estimating the performance of such a transient nature solar dryer. For this purpose, a series of experimental studies are conducted through four successive days and compared with the generalized regression neural network (GRNN) modeling. GRNN architecture proposed in this study consists of three inputs (time duration, irradiance, and ambient temperature) and four outputs (drying chamber temperature, the mass of moisture removed, drying rate, and dryer efficiency). Such generalized regression neural network architecture was trained, tested, and validated with real-time experimental variable data sets. The results of the GRNN model are in good agreement with experimental results. The overall accuracy of the proposed GRNN model in predicting the performance is 96.29%.

scholarly journals Estimation of fastener pull-through resistance of composite laminates based on generalized regression neural network

2020 ◽  
Vol 29 ◽  
pp. 2633366X2096884
Author(s):  
Sheng Mingjian ◽  
Chen Puhui ◽  
Chen Cheng
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Composite Laminates ◽  
Engineering Application ◽  
Transverse Load ◽  
Generalized Regression Neural Network ◽  
Element Analysis ◽  
Research Attention ◽  
Generalized Regression ◽  
Pull Through

The fastener pull-through resistance is a key performance index of composite laminates used for engineering application, and increasing research attention is being paid to developing methods for its calculation or estimation. The currently available research methods mainly focus on the standard test and the finite element analysis for determining the pull-through resistance of composite laminates suffering transverse load by the fasteners. Based on the results of the fastener pull-through resistance experiment performed on X850 composite laminates, a model for estimating the maximum affordable load of composite laminates for the fastener pull-through resistance is proposed, using generalized regression neural network technology. The inputs of this model are simplified to six parameters: the proportion of the ±45° layer of the laminates, the number of the layers, the thickness of the laminates, the bolt head shape, whether the bolt has a washer or not, and the nominal diameter of the bolt; the Gauss function is used as the hidden layer function. The model uses a large portion of the experimental data to train for finding the optimal smoothness factor, which is used to reconstruct the model, and simulation is performed with the remainder of the experimental data. The comparison between the estimated results using the model and the experimental results shows that the generalization ability of the proposed model can meet the estimation requirements. Moreover, the pull-through resistance of composite laminates under transverse load from a fastener can be estimated with high accuracy after some standard fastener pull-through resistance tests of the composite laminates.

scholarly journals Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1631 ◽  
Author(s):  
Zhuo Zhang ◽  
Fayu Sun ◽  
Qingling Li ◽  
Weiqiang Wang ◽  
Dedong Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Generalized Regression ◽  
Supercritical Carbon

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

Risk of Voltage Collapse Using Multi-Layer Feed-Forward Neural Network and Generalized Regression Neural Network

2015 ◽  
Vol 793 ◽  
pp. 483-488
Author(s):  
N. Aminudin ◽  
Marayati Marsadek ◽  
N.M. Ramli ◽  
T.K.A. Rahman ◽  
N.M.M. Razali ◽  
...  
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Risk Index ◽  
Weather Condition ◽  
Generalized Regression Neural Network ◽  
Voltage Collapse ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Generalized Regression

The computation of security risk index in identifying the system’s condition is one of the major concerns in power system analysis. Traditional method of this assessment is highly time consuming and infeasible for direct on-line implementation. Thus, this paper presents the application of Multi-Layer Feed Forward Network (MLFFN) to perform the prediction of voltage collapse risk index due to the line outage occurrence. The proposed ANN model consider load at the load buses as well as weather condition at the transmission lines as the input. In realizing the effectiveness of the proposed method, the results are compared with Generalized Regression Neural Network (GRNN) method. The results revealed that the MLFFN method shows a significant improvement over GRNN performance in terms of least error produced.

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