In this paper we showed the method of resistive gas sensors data processing. The UV irradiation and temperature modulation was applied to improve gas sensors’ selectivity and sensitivity. Noise voltage across the sensor’s terminals (proportional to its resistance fluctuations) was recorded to estimate power spectral density. This function was an input data vector for LS-SVM (least squares – support vector machine) algorithm, which predicted a concentration of gas present in sensor’s ambient atmosphere. The algorithm creates a non-linear regression model at learning stage. This model can be used to predict gas concentration by recording resistance noise only. We have proposed a fast method of selecting LS-SVM parameters to determine high quality model. The method utilizes a behavior of immune system to determine optimal parameters of the LS-SVM algorithm. High accuracy of the applied method was proved for the recorded experimental data.
Autorzy
Informacje dodatkowe
- Kategoria
- Publikacja w czasopiśmie
- Typ
- artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
- Język
- angielski
- Rok wydania
- 2015
