A Least Squares Based Parameter Identification of the Mesic Respiratory System Model

Abstract

A Mesic respiratory system parameter identification is studied in this paper for providing the useful theory and data support in the improvement of human respiratory model accuracy, respiratory disease diagnosis and design of the new ventilator. The Mesic respiratory system model is established based on Simulink platform. The least-square algorithm is then used to do the static and dynamic parameter identification with the theoretical data, clinical data and fitted clinical data. Finally, the validation of the parameter identification is performed by the clinical data. The parameters got by clinical fitting data could reach the physiological characteristics well. The pressure, volume and flow curve is the most similar compared with clinical data. This method provides an efficient way for the identification research of relative models. It is also a supplement of Mesic respiratory system.