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The number of hidden layers and neurons was established by training different feed-forward networks of various topologies and selecting the optimal one based on minimization of the performance function mean square error (MSE).
They are as follows: the first one is a sequential optimization of pipe diameter based on minimization of total cost without considering heat losses and then of insulation thickness based on minimization of cost of insulation and heat losses.
The method incorporates efficient tuning procedures based on minimization of radius/margin and span bound for leave-one-out errors.
The method incorporates an efficient parameter tuning procedure (based on minimization of radius/margin bound for SVM's leave-one-out errors) into a multi-class classification strategy using a fuzzy decision factor, which is named fuzzy support vector machine (FSVM).
The calibration model was optimized by an appropriate selection of number of factors and wavelength regions to be used for building data matrix, based on minimization of predicted error sum of square (PRESS) using leave-one-out cross-validation strategy.
This new approach is based on minimization of crossover workstations.
Back propagation is based on minimization of a suitable error or cost function.
The last algorithm is based on minimization of (6), using the BP procedure available in [32].
Our approach is based on minimization of weighted l2-norm of the residuals.
Our method is based on minimization of a weighted l2-norm of the prediction error.
An optimal recertification interval will be calculated based on minimization of risk to personnel.
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