Hermite Neural Network-based Intelligent Sensors for Harsh Environments

We propose a novel computationally efficient artificial neural network (NN) for design and development of intelligent sensors to operate in harsh environments which can have wide variation of environmental conditions. The proposed Hermite NN (HeNN) models the inverse characteristics of a sensor and can provide linearized sensor response characteristics irrespective of change in environmental conditions, even when the environmental parameters influence the sensor characteristics nonlinearly. By taking an example of a capacitive pressure sensor, we have shown through extensive computer simulations that the HeNN-based model can linearize its response with maximum full scale (FS) error of +/-0.5% when it is operated in a harsh environment with temperature variation of -50 to 200 degrees C and influenced nonlinearly. We have compared performance of the proposed HeNN-based model with a MLP-based model and shown its superior performance in terms of FS error and computational complexity.