Assisted history matching using artificial neural network based global optimization method – Applications to Brugge field and a fractured Iranian reservoir

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.

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

Water ◽

10.3390/w11010085 ◽

2019 ◽

Vol 11 (1) ◽

pp. 85 ◽

Cited By ~ 7

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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Development and Validation of a New PCR Optimization Method by Combining Experimental Design and Artificial Neural Network

Applied Biochemistry and Biotechnology ◽

10.1007/s12010-009-8581-4 ◽

2009 ◽

Vol 160 (1) ◽

pp. 269-279 ◽

Cited By ~ 4

Author(s):

Ye Li ◽

Xueling Du ◽

Qipeng Yuan ◽

Xinhua Lv

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Experimental Design ◽

Optimization Method ◽

Pcr Optimization ◽

Artificial Neural ◽

Development And Validation

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An optimal design of axial-flow fan blades by the machining method and an artificial neural network

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1243/0954406021524954 ◽

2002 ◽

Vol 216 (3) ◽

pp. 367-376 ◽

Cited By ~ 5

Author(s):

B-J Lin ◽

C-I Hung ◽

E-J Tang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tool Path ◽

Three Dimensional ◽

Axial Flow ◽

Integrated Approach ◽

Optimization Method ◽

Surface Geometry ◽

Axial Flow Fan ◽

Artificial Neural

The geometry design and machining of blades for axial-flow fans are important issues because the twisted profile and flowfield of blades are complicated. The rapid design of a blade that performs well and satisfies machining requirements is one of the goals in designing fluid machinery blades. In this study, an integrated approach combining computational fluid dynamics (CFD), an artificial neural network, an optimization method and a machining method is proposed to design a three-dimensional blade for an axial-flow fan. From the machining point of view, the three-dimensional surface geometry of a fan blade can be defined as the swept surface of the tool path created by using the generated machining method. By taking advantage of its powerful learning capability, a back-propagation artificial neural network is used to set up the flowfield models and to forecast the flow performance of the axial-flow fan. The desired optimal blade geometry is obtained by using a complex optimization method.

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