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Fuzzy relations forming the essence of the prediction model are optimized using particle swarm optimization.

Moreover, parameters of the derived mathematical model are optimized by using artificial bee colony algorithm.

Parameters of the inverse neural NARX model are optimized with the modified differential evolution (MDE) algorithm.

The parameters used in the proposed APF based model are optimized through theoretical analysis and simulation experiments.

Secondly, the parameters of the CNN model are optimized from a small training set, while the over-fitting problem of the neural network has been alleviated to some extent.

Fuzzy inference mechanism has been used here for predicting the future values of the controller output while crisp consequent values of the rulebase of the Takagi Sugeno model are optimized using a genetic algorithm.

Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm.

A multi-objectives optimization model is established, and the multi-objectives optimization model is optimized by the non-linear method.

Third, the adjustment coefficient in the new model is optimized to obtain optimal values for the time response function.

First of all, graphene-based DNA sensor model is optimized using particle swarm optimization algorithm.

After that ANN model was optimized by unified particle swarm optimization (UPSO).