Artificial neural network based inverse model control of a nonlinear process

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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An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm

Journal of Intelligent Manufacturing ◽

10.1007/s10845-014-0999-z ◽

2014 ◽

Vol 28 (2) ◽

pp. 473-487 ◽

Cited By ~ 19

Author(s):

Kuo-Ming Tsai ◽

Hao-Jhih Luo

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Injection Molding ◽

Inverse Model ◽

Optical Lens ◽

Artificial Neural

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Non-destructive sensing and its inverse model for canopy parameters using texture analysis and artificial neural network

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2007.03.005 ◽

2007 ◽

Vol 57 (2) ◽

pp. 149-165 ◽

Cited By ~ 34

Author(s):

M. Ushada ◽

H. Murase ◽

H. Fukuda

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Texture Analysis ◽

Inverse Model ◽

Artificial Neural ◽

Non Destructive

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Artificial Neural Network Control of an Experimental Heat Exchanger Facility

10.1115/imece1999-1134 ◽

1999 ◽

Author(s):

Gerardo Díaz ◽

Mihir Sen ◽

K. T. Yang ◽

Rodney L. McClain

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Exchanger ◽

Internal Model Control ◽

Network Control ◽

Integral Control ◽

Experimental Heat ◽

Model Control ◽

Artificial Neural ◽

Fin Tube

Abstract The artificial neural networks technique is applied to control the dynamic behavior of a fin-tube single-row compact heat exchanger. The experimental setup consists of a variable-speed wind-tunnel facility built specifically for heat exchanger analysis. Two different control methodologies were studied. The first one corresponds to adaptive control in which the weights and biases of the artificial neural network that acts as a controller are modified depending on the error obtained between the desired outlet air temperature and its measured value. Experimental results show that the stability of the system varies depending on the different ways of performing the adaptation of the controller. The second control strategy tested corresponds to internal model control. We added a filter and an integral control structure to obtain an offset-free steady state prediction. The control methodology was extensively tested and the results compared to those of conventional PID control. The results were very favorable for the neural controller.

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