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Articles by H. Eliasi
Total Records ( 2 ) for H. Eliasi
  S. Jafari , H.R. Abdolmohammadi , H. Eliasi , M.B. Menhaj and M.R. Rajati
  The aim of this study is to provide an experiment design method for modeling and function approximation. Modeling real-life systems is extremely of interest nowadays. Models could be useful in analysis of systems and help us understand their behavior. From a new point, models could be classified into three classes: black box models, gray box models and white box models. Our idea is related to black box modeling. Proper performance of a black box model depends on structure of the model as well as the data used to determine its parameters. Although one of the important factors affecting the richness of the dataset is the number of data, increasing the number of data points is limited in real problems. For instance gathering data from many systems imposes spending lots of time and cost. In this study, inspired by honey bee algorithm, we have designed a method which enriches the datasets for a known number of data, in comparison to other conventional data extraction methods. In such a method, after extracting some data by grid method, the other data points are extracted according to an intelligent analysis on available data. The results illustrate the efficiency of the proposed method.
  R. Barzamini , A.R. Yazdizadeh , H.A. Talebi and H. Eliasi
  In this study, an adaptive controller is presented that addresses the coupling effects between two groups of electromagnetic trains. The main application of DEM (Double Electro-Magnet) is rapid rail transportation. Since the number of passengers are stochastic, the mass of the train will be variable too. On the other hand, due to the variation of the DEM parameters (such as coil inductance) in a real environment, the system is to be controlled in a proper manner. The proposed method in this study overcomes all of these problems. The module, based on some reasonable assumptions of nonlinear mathematical model, is modeled as a double-electromagnet system. The proposed algorithm has a satisfying performance in tracking in presence of unknown changes in the mass. The advantage of the proposed algorithm in comparison to non-linear controllers is that knowing the mass changes is not necessary. It is also important to make sure that a control system is robust against measurement noises, because all sensors collect noise from the environment. Due to the presence of input and output perturbation, the new proposed algorithm shows satisfying performance. The results show that the proposed method is less sensitive to perturbation in the input.
 
 
 
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