Ventilator Control Based on a Fuzzy-Neural Network Approach
Respiratory systems are complex nonlinear systems that exhibit uncertain properties. Since patients needs are not constant acceptable control of ventilator has to be adaptive to ensure that patients with severe pulmonary disease will obtain the required ventilation support. The AUTOPILOT-BT system was developed to reduce cognitive load of intensivists and at the same time improve mechanical ventilation therapy 1. The goal of this research was to evaluate a nonlinear adaptive fuzzy-neural network controller, in which a fuzzy controller is used to control the flow and a neural network is used to identify the nonlinear respiratory system. The proposed nonlinear intelligent controller is applied to data recorded during a multicenter study on ARDS patients (adult respiratory distress syndrom). The experimental results demonstrate that this adaptive fuzzy-neural network controller has much better control performance than is obtained with traditional controllers.
fuzzy-neural network controller ventilator flow control respiratory mechanics
Hui Zhu Knut M(o)ller
College of Mechanical & Electric Eng. Soochow University Suzhou, Jiangsu, China, 215021 Department of Biomedical Eng. Furtwangen University Villingen-Schwenningen, Germany,78054
国际会议
上海
英文
747-750
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）