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• W2331837875 abstract "We present an efficient method for estimating variables and parameters of a given system of ordinary differential equations by adapting the model output to an observed time series from the (physical) process described by the model. The proposed method is based on (unconstrained) nonlinear optimization exploiting the particular structure of the relevant cost function. To illustrate the features and performance of the method, simulations are presented using chaotic time series generated by the Colpitts oscillator, the three-dimensional Hindmarsh-Rose neuron model, and a nine-dimensional extended Rossler system." @default.
• W2331837875 created "2016-06-24" @default.
• W2331837875 creator A5056288636 @default.
• W2331837875 creator A5086510321 @default.
• W2331837875 date "2011-11-23" @default.
• W2331837875 modified "2023-09-26" @default.
• W2331837875 title "State and parameter estimation using unconstrained optimization" @default.
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• W2331837875 doi "https://doi.org/10.1103/physreve.84.056214" @default.
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• W2331837875 hasPublicationYear "2011" @default.
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