Premature infant incubator system is a vital and critical area because it deals with premature infant or illness baby. It is essential to detect any abnormal conditions occur in the premature infant incubator system as soon as possible. Temperature, humidity, and oxygen concentration are the main parameters must be control in the premature infant incubator system. In this paper novel technique by using Artificial Neural Network ANN is used in order to simulate the premature infant incubator control system by implementing the back propagation method. Sensors are used to indicate temperature, humidity, and oxygen concentration of the incubator internal environment. Sensors output are entering to the ANN, which identify the corresponding case and decide the suitable reaction upon previous training. The proposed ANN premature incubator control system in all conditions that can occur in the premature infant incubator environment proved right decision. |
