This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for system identification. Although DNNs show impressive approximation ability in various fields, several challenges still exist identification problems. First, are known to be too complex that they can easily overfit the training data. Second, selection input regressors is nontrivial. Third, uncertainty quantificati...

The researcher measured the least number of frequency response functions required for the identification of modal parameters, in order to simplify the identification of modal properties of such systems. In this work, the orbital machines are supposed to be a combination of orbital and non-orbital components. Structural Approach specified the identification of dynamic properties only to those ph...

Continuum robotic arms that are inspired from nature, have many advantages compared to traditional robots, which motivate researchers in this field. Dynamic modeling and controlling these robots are challenging subjects due to complicated nonlinearities and considerable uncertainties existing in these structures. In this paper, first a dynamic three-dimensional model of the continuum robotic ar...

  Abstract: In this paper, Artificial Neural Network (ANN) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. The actual input-output data of the system were measured in order to be used for system identification based on root mean square error (RMSE) minimization approach. It was shown that the designed recurrent neural network is able to pr...

neural networks are applicable in identification systems from input-output data. in this report, we analyze thehammerstein-wiener models and identify them. thehammerstein-wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...