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Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system

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Abstract

In this study, strength of evolutionary computational intelligence based on genetic algorithms (GAs) is exploited for parameter identification of nonlinear Hammerstein controlled autoregressive (NHCAR) systems. The fitness function is constructed for the NHCAR system by defining an error function in the mean square sense. Unknown adjustable weights of the system are optimized with GAs, used as an effective tool for effective global search. Comparative analysis of the proposed scheme is made from true parameters of the systems for a number of scenarios based on different levels of signal-to-noise ratios. The validation of the performance is made through statistics based on sufficiently large number of runs using indices of mean absolute error, variance account for, and Thiel’s inequality coefficient as well as their global versions.

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Authors and Affiliations

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

    Muhammad Asif Zahoor Raja, Abbas Ali Shah & Ammara Mehmood

  2. Department of Electronic Engineering, International Islamic University, Islamabad, Pakistan

    Naveed Ishtiaq Chaudhary

  3. Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, Pakistan

    Muhammad Saeed Aslam

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  1. Muhammad Asif Zahoor Raja
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Correspondence to Naveed Ishtiaq Chaudhary.

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Raja, M.A.Z., Shah, A.A., Mehmood, A. et al. Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system. Neural Comput & Applic 29, 1455–1474 (2018). https://doi.org/10.1007/s00521-016-2677-x

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