Hybrid State Estimation Combining Artificial Neural Network and Physical Model

2019 ◽  
Author(s):  
Philipp Maximilian Sieberg ◽  
Sebastian Blume ◽  
Nele Harnack ◽  
Niko Maas ◽  
Dieter Schramm
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
State Estimation ◽  
Physical Model ◽  
Hybrid State ◽  
Artificial Neural

Studying on the Fiber Diameter of Polypropylene (PP) Spunbonding Fabric by Means of Artificial Neural Network Model and Physical Model

2010 ◽  
Vol 426-427 ◽  
pp. 356-360
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Good Prediction ◽  
Artificial Neural

In this work, the artificial neural network model and physical model are established and utilized for predicting the fiber diameter of polypropylene(PP) spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the physical model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Quarter Circular Breakwater: Prediction of Transmission Using Multiple Regression and Artificial Neural Network

2014 ◽  
Vol 48 (1) ◽  
pp. 92-98 ◽  
Author(s):  
Rushil Goyal ◽  
Kriti Singh ◽  
Arkal Vittal Hegde
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Physical Model ◽  
Multiple Regression ◽  
Mean Squared Error ◽  
Nonlinear Process ◽  
Wave Transmission ◽  
Physical Models ◽  
Mathematical Tool ◽  
Artificial Neural

AbstractThe physical model study of coastal structures is a nonlinear process influenced by innumerable parameters. As a result of a lack of definite systems, intricacies, and high costs involved in the physical models, we need a simple mathematical tool to predict wave transmission through quarter circular breakwater (QBW). QBW is a state-of-the-art breakwater essentially based on the exploitation of the concepts of semicircular breakwater. This paper discusses the use of soft computing tools such as MATLAB-based multiple regression (MR) and artificial neural network (ANN) to predict the wave transmission coefficient of QBW. To assess the accuracy of the proposed model and its ability to forecast, correlation coefficient and mean squared error are availed. On comparing the results obtained from MR and ANN, it is concluded that ANN gives more accurate results and can be used as a powerful tool for the modeling of hydrodynamic breakwater transmission through QBW. It serves as a viable alternative to the conventional physical model to simulate the hydrodynamic transmission performance of QBW.

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