TY - GEN
T1 - An evolutionary intelligent approach for the LTI systems identification in continuous time
AU - Morales, Luis
AU - Camacho, Oscar
AU - Chávez, Danilo
AU - Aguilar, José
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Genetic algorithms
KW - Particle swarm optimization
KW - System identification
KW - System modeling
UR - https://www.scopus.com/pages/publications/85059768546
U2 - 10.1007/978-3-030-05532-5_32
DO - 10.1007/978-3-030-05532-5_32
M3 - Contribución a la conferencia
AN - SCOPUS:85059768546
SN - 9783030055318
T3 - Communications in Computer and Information Science
SP - 430
EP - 445
BT - Technology Trends - 4th International Conference, CITT 2018, Revised Selected Papers
A2 - Botto-Tobar, Miguel
A2 - D’Armas, Mayra
A2 - Zúñiga Sánchez, Miguel
A2 - Zúñiga-Prieto, Miguel
A2 - Pizarro, Guillermo
PB - Springer Verlag
T2 - 4th International Conference on Technology Trends, CITT 2018
Y2 - 29 August 2018 through 31 August 2018
ER -