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For updating the parameters of the fuzzy system, an adaptation proportional-integral (PI) law is proposed.
Moreover, a recursive algorithm for online updating the parameters of the failure prognosis model is developed.
By updating the parameters of the estimated model based on prediction error, the estimated model approaches the real plant gradually in real-time.
These uncertainties in the response increase with frequency and have to be taken into account when updating the parameters of the computational model in order to obtain a robust estimation of these parameters.
Each iteration involves generating a random sample according to a probability distribution and then updating the parameters of the probability distribution in order to produce better samples in the next iteration.
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We repeat the squared distance minimization and update the parameters of the quadratic curve and surface by iterations until convergency.
Considering the economy and security, a closed-loop identification method is adopted to update the parameters of the model.
Abstract: We update the parameters of the fitted functions for lepton and jet selection efficiencies presented in section 9 of the original paper.
This specialized learning neurofuzzy controller uses the back-propagation gradient descent method to update the parameters of the network through time.
An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller.
Its output is used as an error signal by an incremental learning algorithm to update the parameters of the neuro-fuzzy controller.
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