I am currently working on a model from a circuit that includes diodes and time varying terms. I have the experimental data and I want to find the best parameters to fit the data with my model obtained from physical laws.
Thank you!
Dear Diary R. Sulaiman
, thanks for your response. I know it is a wide topic, but do you know any technique that is used to cover a more general range of nonlinear systems for parameter identification? what else do you need to give you an idea of my problem. Best regards!
Dear A.-J. Guel-Cortez,
One of the effective ways to parameters' identification is the Tikhonov regularization method. This method can be successfully applied for identification of nonlinear system parameters. If by some reasons you have not possibility to apply this method yourself, I hope that I can help you if you send me your parametrized nonlinear system.
Best regards,
Sharif Guseynov
Maybe you can consider the recursive least squares algorithm (RLS) which allows for (real-time) dynamical application of least squares (LS) regression to a time series of time-stamped continuously acquired data points. As with LS, there may be several correlation equations with the corresponding set of dependent (observed) variables. RLS is the recursive application of LS, so that each new data point is taken in account to modify (correct) a previous estimate of the parameters from some linear (or linearized) correlation thought to model the observed system (possibly nonlinear). For RLS with forgetting factor (RLS-FF), acquired data is weighted according to its age, with increased weight given to the most recent data. This is often convenient for adaptive control and/or real-time optimization purposes.
Application example ― While investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS-FF algorithm to estimate the parameters from the KLa correlation, used to predict the O2 gas-liquid mass-transfer. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). Simulations assessed the effect of numerically generated white Gaussian noise (2-sigma truncated) and of first order delay.
Thesis Controlo do Oxigénio Dissolvido em Fermentadores para Minimi...
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