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Cited by 10 publications
(4 citation statements)
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“…The availability of relevant data for the disease will also enhance the model parameters estimation and the usefulness of the model with respect to the disease involved. Model parameters from epidemiological models can be easily estimated using methods involving ordinary least squares estimator, maximum likelihood estimator derivative approximation, moments estimator, Markov chain Monte Carlo (MCMC) strategy, derivative-free optimization algorithms, the Levenberg-Marquardt, and Trust-Region-Reflective [22][23][24][25][26]. Biegler and Grossmann [27] employed optimization technique based on the seasonal data with the SIRS epidemic model as a constraint in order to estimate the parameters of a generalized incidence rate function.…”
Section: Introductionmentioning
confidence: 99%
“…The availability of relevant data for the disease will also enhance the model parameters estimation and the usefulness of the model with respect to the disease involved. Model parameters from epidemiological models can be easily estimated using methods involving ordinary least squares estimator, maximum likelihood estimator derivative approximation, moments estimator, Markov chain Monte Carlo (MCMC) strategy, derivative-free optimization algorithms, the Levenberg-Marquardt, and Trust-Region-Reflective [22][23][24][25][26]. Biegler and Grossmann [27] employed optimization technique based on the seasonal data with the SIRS epidemic model as a constraint in order to estimate the parameters of a generalized incidence rate function.…”
Section: Introductionmentioning
confidence: 99%
“…The stochastic gradient (SG) parameter estimation algorithm has less computational burden than the least squares algorithm [10,11], and has been applied to study different types of systems, e.g. multivariable systems [12,13] and non-linear systems [14][15][16]. Although the SG identification algorithm is widely employed for parameter estimation, it has a slow convergence rate [17].…”
Section: Introductionmentioning
confidence: 99%
“…The identification and control of nonlinear system have been widely studied in recent years [1,2,3,4]. Before the design of a controller, it is necessary to achieve system identification.…”
Section: Introductionmentioning
confidence: 99%