Improved identification method of pulmonary elastance fuzzy model based on pre-adjustment of membership functions

In artificial respiration, setting of ventilation conditions is expected to fit each patient. Characteristics of people´s respiratory systems are very different and those values are very difficult to be measured directly. The aim of our research is to establish a framework to modeling the respiratory system and to set appropriate ventilation conditions based on the characteristics estimation to fit individual patient. For this purpose, an iterative estimation method of fuzzy pulmonary elastance model has been proposed by authors in the previous works. In this paper, an improved identification method is addressed in which an optimal pre-adjustment procedure of membership functions of fuzzy variables is incorporated in the iterative estimation method. A numerical example based on real clinical data is shown to illustrate the improvement in proposed algorithm.