Method and apparatus for operating a gas sensor
A method and apparatus for operating a gas sensor are disclosed. In an embodiment a method for operating a gas sensor includes providing, by at least one gas sensor element, a sensing signal and correcting, by a neural network, the sensing signal, wherein the neural network comprises an input layer, an output layer and at least one hidden layer, wherein the input layer comprises a given number k>1 of input neurons for each gas sensor element, and wherein a respective gas sensor element provides its sensing signal to one of the corresponding input neurons dependent on a measurement parameter applied to the at least one gas sensor element.
1. A method for operating a gas sensor, the method comprising:
providing, by at least one gas sensor element, a sensing signal; and
correcting, by a neural network, the sensing signal,
wherein the neural network comprises an input layer, an output layer and at least one hidden layer,
wherein the input layer comprises a given number k>1 of input neurons for each gas sensor element, and
wherein a respective gas sensor element provides its sensing signal to one of the corresponding input neurons dependent on a measurement parameter applied to the at least one gas sensor element.
2. The method according to claim 1 , wherein the gas sensor is operable in a learning mode using the neural network adjusted with random feedback loops between directly adjacent layers.
3. The method according to claim 2 , wherein the random feedback loops comprise different temperature values.
4. The method according to claim 2 , wherein the random feedback loops comprise different electrical field strength values.
5. The method according to claim 1 , wherein the gas sensor is operable in a normal operation mode using the neural network with a feedforward structure.
6. The method according to claim 1 , wherein the at least one gas sensor element comprises a semiconductor substrate with a metal oxide sensing layer.
7. The method according to claim 1 , wherein the at least one gas sensor element comprises electrically conductive polymers.
8. The method according to claim 1 , wherein the measurement parameter applied to the at least one gas sensor element is representative of a temperature of a sensing layer of the at least one gas sensor element.
9. The method according to claim 1 , wherein the measurement parameter applied to the at least one gas sensor element is representative of an electrical field strength.
10. A gas sensor system comprising:
at least one gas sensor element configured to provide a sensing signal; and
an apparatus configured to receive the sensing signal and comprising a neural network configured to correct the sensing signal,
wherein the neural network comprises an input layer, an output layer and at least one hidden layer,
wherein the input layer comprises a given number k>1 of input neurons for each gas sensor element, and
wherein a respective gas sensor element is configured to provide its sensing signal to one of the corresponding input neurons of the respective gas sensor element dependent on a measurement parameter applied to the respective gas sensor element.
11. The gas sensor system according to claim 10 , further comprising an application-specific integrated circuit (ASIC) configured to provide random feedback weights to the neural network.
12. The gas sensor system according to claim 11 , wherein the ASIC is configured to execute processing of a trained neural network in a normal operation mode of the gas sensor system.
13. The gas sensor system according to claim 11 , wherein the ASIC is configured to:
provide learning data; and
provide random feedback weights for random feedback loops to the neural network for executing processing of the neural network in a learning mode.