The Extreme Learning Machine for the Extraction of Mono-Exponential Decay Time

Abstract

The mono-exponential decay has been used to describe various physical phenomena such as cavity ring-down signal, fluorescence decay, etc. In this paper, a neural network method of extreme learning machine (ELM) is adopted to efficiently extract decay time. The theoretical extraction precision, accuracy, and computation cost are all preliminarily analyzed and quantitatively compared with the traditional Levenberg-Marquardt (LM) algorithm. The training set and the testing set for the ELM are built based on our experimental system parameters. After dataset training, the ELM model for mono-exponential decay extraction is obtained. In the dataset testing, this model gives almost the same results with the LM algorithm. The relative deviation of precision is only about ±1 nanosecond. This ELM model can also be directly used in experimental cavity ring-down system. Comparing with the LM algorithm, the relative deviations are less than ±2 nanoseconds when the decay time is in the range of 0.98 μs∼2.20 μs. The ELM method for mono-exponential decay extraction has high efficiency and fine robustness. It has the potential for the applications in cavity ring-down spectroscopy, fluorescence decay analysis, and nuclear radioactive technique, etc.