Hybridizing Optimization Method and Artificial Neural Network for Urban Drainage System Design

2020 ◽  
pp. 1055-1062
Author(s):  
Soon Ho Kwon ◽  
Donghwi Jung ◽  
Joong Hoon Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
System Design ◽  
Drainage System ◽  
Optimization Method ◽  
Urban Drainage ◽  
Urban Drainage System ◽  
Artificial Neural

A Novel Global Optimization Method to Design Laser Parameters with Artificial Neural Network and Genetic Algorithm

2010 ◽  
Vol 37 (5) ◽  
pp. 1203-1208
Author(s):  
肖光宗 Xiao Guangzong ◽  
龙兴武 Long Xingwu ◽  
张斌 Zhang Bin ◽  
吴素勇 Wu Suyong ◽  
赵洪常 Zhao Hongchang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Global Optimization ◽  
Optimization Method ◽  
Global Optimization Method ◽  
Laser Parameters ◽  
Artificial Neural

scholarly journals Uncertainty Analysis in the Evaluation of Extreme Rainfall Trends and Its Implications on Urban Drainage System Design

Water ◽  
2015 ◽  
Vol 7 (12) ◽  
pp. 6931-6945 ◽  
Author(s):  
Vincenza Notaro ◽  
Lorena Liuzzo ◽  
Gabriele Freni ◽  
Goffredo La Loggia
Keyword(s):  
Uncertainty Analysis ◽  
System Design ◽  
Drainage System ◽  
Extreme Rainfall ◽  
Urban Drainage ◽  
Urban Drainage System ◽  
Rainfall Trends

RF MEMS for Reconfigurable Antenna Using Gravitational Search Optimization and Artificial Neural Network

2018 ◽  
Vol 27 (08) ◽  
pp. 1850132
Author(s):  
T. A. Balarajuswamy ◽  
R. Nakkeeran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rf Mems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Beam Width ◽  
Optimization Method ◽  
Gravitational Search ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The projected method explains about the problems occurred in the combination of the MEMS switches and the complete scheme plan is resolved through choosing the finest devise limits for the plan. The devise limits, namely, length of beam, width of beam, torsion arm length, switch thickness, holes and gap were measured. At this point, the finest value of the devise limit is forecast by the aid of artificial neural network (ANN). Furthermore, the method contains the optimization method of Gravitational Search Algorithm (GSA) to optimize the input signal and so dropping the Mean Square Error (MSE). The complete scheme is executed in the operational platform of MATLAB and the outcomes were examined.

scholarly journals Applicability of ε-Support Vector Machine and Artificial Neural Network for Flood Forecasting in Humid, Semi-Humid and Semi-Arid Basins in China

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Thabo Michael Bafitlhile ◽  
Zhijia Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Optimization Method ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Climatic System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Semi Arid

The aim of this study was to develop hydrological models that can represent different geo-climatic system, namely: humid, semi-humid and semi-arid systems, in China. Humid and semi-humid areas suffer from frequent flood events, whereas semi-arid areas suffer from flash floods because of urbanization and climate change, which contribute to an increase in runoff. This study applied ɛ-Support Vector Machine (ε-SVM) and artificial neural network (ANN) for the simulation and forecasting streamflow of three different catchments. The Evolutionary Strategy (ES) optimization method was used to optimize the ANN and SVM sensitive parameters. The relative performance of the two models was compared, and the results indicate that both models performed well for humid and semi-humid systems, and SVM generally perform better than ANN in the streamflow simulation of all catchments.

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