Neuro fuzzy prediction of laser fluence based on photoacoustic signal analysis in different gas mixtures

Abstract

Photoacoustic spectroscopy achieves high sensitivity and selectivity using high-power lasers. Variations of parameters such as laser beam spatial profile and fluence Φ can alter precision of photoacoustic measurements. Numerous commercial instruments aren’t usable for high Φ values measurement, due to possible harmful effects. To estimate high Φ values from photoacoustic signals in time domain we applied computational intelligence method: adaptive-network-based fuzzy inference system (ANFIS). Experimental photoacoustic signals are generated in two different gas mixtures: SF6 + Ar and C2H4 + Ar, for Φ values ranging from (0.2–1.4) J∙cm-2. Obtained results indicate that different absorption characteristics of examined molecules and various signal intensities don’t influence ANFIS prediction, due to its adaptation ability and error tolerance in dealing with imprecise and noisy data. Furthermore, robustness, high learning capability and self-correction, make this technique computational effective and recommendable for in situ photoacoustic measurements. Aside from many advantages, limitations of the proposed method are also discussed.