Abstract
Some types of synthetic polymers have been tested in nitric oxide sensing. Feed-forward neural networks with different types and topologies are used in mathematical modeling of the system, to predict the voltage of the sensor as function of polymer type, gas concentration and time. In this way, the efficiency of the sensor can be appreciated. The relative errors, extremely low, obtaining in the validation phase, prove the validity of the neural models. The optimization problem performed by inverse neural network modeling answers the question what are the initial conditions that lead to an imposed value for output signal of the sensor.
Cuvinte cheie
neural networks
mathematical modeling
polymer-based sensors
direct and inverse modeling
nitric oxide