To keep the high performance of the control scheme in presence of rotor and stator resistances variations due to temperature rise, the control algorithm is online updated by the estimated resistances values at each sample time.
Then, the finite element model is successfully updated by comparing the estimated results of the operational modal analysis.
For one iteration of the proposed algorithm, information of the desired user and the virtual user are obtained in the symbol detection module, then the parameters are estimated and updated by the parameter estimation module.
The remaining possible models are updated by first calculating an estimated continuous-time transfer function, which results to be identical for all the models while their discretized versions are distinct in general.
Step 4 Select M best individuals from (mathrm{Pop}(t)) to form the parent set (Q t)) by the truncation selection, and update the probability model by estimating the distribution of (Q t)).
With a generalized Baum Welch′s auxiliary function and the Jensen inequality based on generalized interval, the GHMM parameters are estimated and updated by the lower and upper bounds of observation sequences.
The estimates are updated by the SISO decoders in every iteration, using the most recent channel estimate.
Each backward track state estimate is updated by applying ({boldsymbol {z}_{k,i}^{b}}) in the Kalman filter update equation.
where y ~ b, ℓ = y b - c - 1 S b g ( ϑ ̂ b and z ℓ = ϑ ̂ b - 1 - ϑ ̂ b . The initial estimate is updated by the optimal increment ϑ ̂ b ( ℓ + 1 ) = ϑ ̂ b + ϑ ~.
