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Parameter estimation for Hammerstein control autoregressive systems using differential evolution

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Abstract

In the present study, strength of stochastic computational paradigms is investigated for parameter estimation of Hammerstein control autoregressive (HCAR) model by exploiting differential evolution, genetic algorithms and pattern search methods. Multidimensional and nonlinear nature of the problem emerging in digital signal systems along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of stochastic solvers to ensure convergence and avoid trapping in local minima. The performance of meta-heuristic approaches is validated through statistical performance indices based on absolute error, weight deviations and mean squared error. Comparative studies of HCAR system identification established efficacy of the designed methodology based on differential evolution over its counterparts.

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Authors and Affiliations

  1. Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan

    Ammara Mehmood

  2. School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia

    Muhammad Saeed Aslam

  3. Department of Electrical Engineering, International Islamic University, Islamabad, Pakistan

    Naveed Ishtiaq Chaudhary

  4. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan

    Aneela Zameer

  5. Department of Electrical Engineering, COMSATS Institute of Information Technology, Attock, Pakistan

    Muhammad Asif Zahoor Raja

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  1. Ammara Mehmood
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Correspondence to Muhammad Saeed Aslam.

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Mehmood, A., Aslam, M.S., Chaudhary, N.I. et al. Parameter estimation for Hammerstein control autoregressive systems using differential evolution. SIViP 12, 1603–1610 (2018). https://doi.org/10.1007/s11760-018-1317-6

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  • DOI: https://doi.org/10.1007/s11760-018-1317-6

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