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Multi objective calibration based on error minimization of temperature and soil moisture model outputs is performed in a semi arid environment.
In this paper, through extension of the present methods and based on error minimization, two fast and efficient layer-by-layer pre-training methods are proposed for initializing deep neural network (DNN) weights.
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To achieve maximized efficiency, this work proposed a method based on error compensation and minimization.
Envy is often based on error.
In this paper, such a nonparametric approach is applied to the design of optimal predictors and discrete-time models based on prediction error minimization by interpreting the predictor impulse responses as realizations of Gaussian processes.
It's flip-flopping based on errors.
The algorithms that will be elaborated upon in this section are all based on square-error minimization.
The details of the evaluation method, based on a squared error minimization, are discussed.
Real transient measurement data from the plant has been used for the identification that is based on standard prediction error minimization.
The procedure is based on a constrained error minimization approach and uses only experimentally identified complex modes and complex natural frequencies together with, for the non-viscous model, the mass matrix of the system.
In this work, parameter estimation technique based on a prediction error minimization (PEM) framework has been used.
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