Abstract:It needs to control ventilator parameters according to individual differences of patient in the auxiliary treatment of mechanical ventilation. In this study, the mechanical model of a respiration system based on general regression neural network（GRNN）are analyzed. To identify parameters of the respiratory system model, a fusion method based on PSO_GRNN, numerical integration and recursive least square is proposed. The static lung pressure value of singlecycle respiratory samples is calculated by direct calculation and the second order polynomial is used to fit the volume error. The mean absolute error of static data points for ten inhalation cycles is 0169 3 mL, and the mean absolute error of static data points for ten expiratory cycles is 0372 8 mL. PSO_GRNN is used to predict the static lung pressure of the multicycle respiratory sample set. For the ten sample sets of respiratory cycle, the average error of the training set is 0009 1 and the average error of the test set is 0406 5. Simulation results show that PSO_GRNN is better than PSO_BP in terms of convergence rate, average error and computation speed. The proposed method can provide an effective reference basis for doctors to set ventilator parameters during the mechanical ventilation treatment. .txt