List of Articles Feed Forward Neural Network

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  1 - Defects Detection of Rotating Machine Using ‎Vibration Analysis and Neural Network ‎
  Seyed Majid Ataei Ardestani
  The base of diagnosing the possible defects of a machine is comparing the frequency ‎spectra of the vibrations at different points with the existing reference spectra. Due to the ‎needless stoping of machine for investigation of its various parts, use of this &l More

  The base of diagnosing the possible defects of a machine is comparing the frequency ‎spectra of the vibrations at different points with the existing reference spectra. Due to the ‎needless stoping of machine for investigation of its various parts, use of this ‎troubleshooting method is affordable; Also, regarding to progress of possible ‎defectes, the machine can be rapaired in any required times. In this study , using ‎Neural Network (MLP and FNN), firstly common defects in rotating machines were created ‎separately, then the produced vibrational frequency were measured by ADASH 4400 ‎analyzer. Introducing four vibrational characteristics including angular misalignment, ‎clearance, failure and unbalance of bearing as input data of artificial neural network ,the ‎results were compared to the reference frequency signals. The results show that neural ‎networks MLP and FNN increase the defects detection ability by 73% and 78%, ‎respectively. So, FNN method is proposed for useful life prediction and detection of rotating ‎parts.‎ Manuscript profile
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  2 - Comparing the Performance of Linear and Non-Linear Models to Explain Almost Ideal Demand System
  Mohammad Rezaei pour Mehdi Zolfaghari mojtaba yousefi dindarloo Abolfazl Najarzadeh
  In most of empirical studies based on almost ideal demand system (Aids), the elasticity of the price and income estimated by these equations resulted to some sensitive policy making recommendations in microeconomics and macroeconomics. It is in such a case that there is More

  In most of empirical studies based on almost ideal demand system (Aids), the elasticity of the price and income estimated by these equations resulted to some sensitive policy making recommendations in microeconomics and macroeconomics. It is in such a case that there is some doubt about reliability of linear estimation of such models. In this study, the performance of linear and non-linear almost ideal demand system is under the investigation. For this purpose, seemingly unrelated regression (SURE) method will be applied to estimate linear model and multilayered feed forward neural network (MFNN) is used to estimate a non-linear one. The results indicate that multilayered feed forward neural network is associated with less error than the linear model, and consequently, leads to a better estimation of almost ideal demand system. This result creates some hesitate on application of Stone price index for linear zing estimation of almost ideal demand system. Therefore, it is suggested that feed forward neural network will be applied to estimate almost ideal demand systems. Manuscript profile