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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 10/2019

05.03.2018 | Original Article

Nature-inspired heuristic paradigms for parameter estimation of control autoregressive moving average systems

verfasst von: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Rabia Bibi, Naveed Ishtiaq Chaudhary, Muhammad Saeed Aslam

Erschienen in: Neural Computing and Applications | Ausgabe 10/2019

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Abstract

Aim of this research is to explore the strength of evolutionary and swarm intelligence techniques for parameter identification of control autoregressive moving average (CARMA) systems. The fitness function for CARMA system identification problem is formulated through error function created in mean square sense, and learning of unknown parameters of the system model is carried out with an effective global search techniques based on genetic algorithms and particle swarm optimization algorithm. Comparative study of the design methodology is conducted from actual parameters of the systems for different values of noise variance and degree of freedom in CARMA identification model. The correctness of the proposed scheme is validated through the results of various performance measures based on mean absolute error, mean weight deviation, variance account for and Theil’s inequality coefficient, and their global variants for sufficiently large number of independent runs.
Vorheriger Artikel Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques
Nächster Artikel GEP and MLR approaches for the prediction of reference evapotranspiration

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Metadaten
Titel
Nature-inspired heuristic paradigms for parameter estimation of control autoregressive moving average systems
verfasst von
Ammara Mehmood
Aneela Zameer
Muhammad Asif Zahoor Raja
Rabia Bibi
Naveed Ishtiaq Chaudhary
Muhammad Saeed Aslam
Publikationsdatum
05.03.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3406-4

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