An Evolutionary Intelligent Approach for the LTI Systems Identification in Continuous Time

  • Luis MoralesEmail author
  • Oscar Camacho
  • Danilo Chávez
  • José Aguilar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)


Identification and modeling of systems are the first stage for development and design of controllers. For this purpose, as an alternative to conventional modeling approaches we propose using two methods of evolutionary computing: Genetic Algorithms (GA) and Particle Swarm Optimization (PSO to create an algorithm for modeling Linear Time Invariant (LTI) systems of different types. Integral Square Error (ISE) is the objective function to minimize, which is calculated between the outputs of the real system and the model. Unlike other works, the algorithms make a search of the most approximate model based on four of the most common ones found in industrial processes: systems of first order, first order plus time delay, second order and inverse response. The estimated models by our algorithms are compared with the obtained by other analytical and heuristic methods, in order to validate the results of our approach.


System modeling System identification Genetic algorithms Particle swarm optimization 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Luis Morales
    • 1
    Email author
  • Oscar Camacho
    • 1
  • Danilo Chávez
    • 1
  • José Aguilar
    • 2
  1. 1.Dpto. de Automatización y Control IndustrialEscuela Politécnica NacionalQuitoEcuador
  2. 2.Universidad de Los AndesMéridaVenezuela

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