Adaptively trained artificial neural network identification of left ventricular assist device

Presents a Neural Network Identification (NNI) method for modeling of the highly complicated nonlinear and time varying human system with a pneumatically driven mock circulation system and Left Ventricular Assist Device (LVAD). This system consists of electronic circuits and pneumatic driving circuits. The initiation of systole and the pumping duration can be determined by a computer program. The line pressure from a pressure transducer inserted in the pneumatic line was recorded. System modeling is completed using the adaptively trained backpropagation learning algorithms with input variables, Heart Rate (HR), Systole-Diastole Rate (SDR), which can vary the state of the system, and preload and afterload, which indicate the systemic dynamic characteristics, and output parameters are preload and afterload