Sentence examples for minimizes the prediction from inspiring English sources

However, when there is a model-plant mismatch, the set of parameter estimates that minimizes the prediction error in the identification problem may not predict the gradients of the optimization objective accurately.

Based on the sample data collected from OXTA in the industrial CSTR, heuristic differential evolution algorithm is employed to adjust the weights and thresholds of the rate constant ANN in such a way that it minimizes the prediction error of the macrokinetic model, and thus the optimal weights and thresholds are obtained and the macrokinetic model of OXTA in the industrial CSTR is developed.

The proper variables are selected in two stages using a multi-objective optimization approach: in the first stage, a specially designed genetic algorithm minimizes the prediction error over a monitoring data set, while in the second stage a simulated annealing technique aims at the reduction of the number of explanatory variables.

Finding the best value/type for each network parameter involves finding a value/type for that parameter which minimizes the prediction error.

Repeat steps (ii)–(iv) for k = 1,…,K, and define the prediction error as PE = 1 n ∑ t = 1 n { x ( t ) − r λ k ( t ) } 2. Finally, by using an optimization algorithm such as golden section search algorithm, we select the smoothing parameter λ value that minimizes the prediction error PE.

This procedure is repeated 10 times, and the model with the combination of loci that maximizes the cross-validation consistency and minimizes the prediction error is selected.

In SVR, the structural risk minimization is implemented for minimizing the upper bound of generalization error, while ANN takes the advantage of empirical risk minimization to minimize the prediction error (Asoodeh and Bagheripour 2013).

Although it is preferable to minimize the prediction error instead of predicting the error, the problem of minimization of the prediction error is as difficult as numerical weather prediction.

However, the algorithm may not necessarily minimize the prediction error of the test dataset itself; thus, some degree of mismatch between the predicted and actual fibrosis scores is expected.

The procedure is essentially based on imposing additional constraints on the allocation of eigenvalues of the filter's transition matrix and on minimizing the prediction error.

A sensitivity analysis was performed by varying the network parameters to minimize the prediction error and determine the optimum network configuration.