Then, by the adaptive control, and the random optimization method, parameters identification is approached.
The proposed method uses the pure sinusoidal model in combination with the algebraic derivative method for parameter identification.
The results show that the proposed method gives parameter identification with good quality for short accelerations using the ARMA model and the bisection method for the SDOF system.
Furthermore, a new method for parameter identification based on IWG and an optimization algorithm are also developed for shear frame structures, and a simultaneous identification of structural parameters and excitation is implemented.
It presents a novel approach to determine the material model parameters which utilizes a new, adjustable stress state specimen, Digital Image Correlation (DIC) measurements, and an inverse method for parameter identification.
In a recent work, we developed an algebraic method for parameter identification of circuit models for batteries by applying discharging tests after variable and possibly very short rest time.
This paper describes a new method for parameter identification for stochastic systems based on a new evaluation measure of the statistical independence between an input signal and extraneous noise.
In the previous section, we introduced the principle method for parameter identification of manipulators, i.e. parameter estimation using nonlinear least-squares and a kinematic model of the manipulator geometry.
Integration of the material balances along with the differential equations resulting from the sensitivity method for parameter identification is carried out for this simulation case with the dde23 MATLAB function for the studies with delays, and with the ode23 MATLAB function for the ones without delays.
Overall, this provides a strong case for the advantages of the Dimensional Reduction Method over current parameter identification methods designed for addressing ill-posed problems.
