â  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...

in this paper, we describe our implementation of an interior point algorithm for large scale systems. first we identify system with small and medium methods convex optimization, then we use interior point method for identification. finally we offer an interior point method that uses nonlinear cost function and see that we achieve a good trade-off between error and cpu time. actually, in this pa...

A system identification methodology that makes use of data mining techniques to improve the reliability of identification is presented in this paper. An important aspect of this methodology is the generation of a population of candidate models. An indication of the reliability of system identification can be obtained through an examination of characteristics of the population. This paper presen...

System identification techniques are powerful tools that help improve modeling capabilities of real world dynamic systems. These techniques are well established and have been successfully used on countless systems in many areas. However, wind turbines provide a unique challenge for system identification because of the difficulty in measuring its primary input: wind. This thesis first motivates ...

Coherent anti-Stokes Raman scattering (CARS) microscopy is a label-free imaging technique that is capable of real-time, nonperturbative examination of living cells and organisms based on molecular vibrational spectroscopy. Recent advances in detection schemes, understanding of contrast mechanisms, and developments of laser sources have enabled superb sensitivity and high time resolution. Emergi...