Modeling and compensation for capacitive pressure sensor by RBF neural networks

Capacitive differential pressure sensor (CPS) is extremely used in industries. This sensor measures pressure and shows current. Accuracy of capacitive differential pressure sensor is limited because the ambient temperature has adverse effects on CPS output characteristic. In order to overcome this limitation, the output of this sensor is compensated by using RBF neural network and because of the importance of modeling of sensors and for having more correct read out, the model of this sensor is extracted by RBF neural network too. A test bench is designed and implemented to data acquisition in a real environment. The experimental results are being used to verify the performance of RBF neural network based on compensating and modeling of nonlinear system of CPS.