Neural Network Based Industrial Processes Monitoring

  • Luis P. Sánchez-Fernández
  • Cornelio Yáñez-Márquez
  • Oleksiy Pogrebnyak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This industrial processes monitoring based on a neural network presents low run-time, and it useful for critical time tasks with periodic processing. This method allows the time prediction in which a variable will arrive to abnormal or important values. The data of each variable are used to estimate the parameters of a continuous mathematical model. At this moment, four models are used: first-order or second-order in three types (critically damped, overdamped or underdamped). An optimization algorithm is used for estimating the model parameters for a dynamic response to step input function, because this is the most frequent disturbance. Before performing the estimation, the most appropriate model is determined by means of a feed-forward neural network.


Order System Circular Buffer Intelligent Data Analysis Transitory Response Continuous Mathematical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Luis P. Sánchez-Fernández
    • 1
  • Cornelio Yáñez-Márquez
    • 1
  • Oleksiy Pogrebnyak
    • 1
  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico CityMexico

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