  While neural networks have been successfully applied to the full-spectrum
k-distribution (FSCK) method at a large range of thermodynamics with k-values
predicted by a trained multilayer perceptron (MLP) model, the required a-values
still need to be calculated on-the-fly, which theoretically degrades the FSCK
method and may lead to errors. On the other hand, too complicated structure of
the current MLP model inevitably slows down the calculation efficiency.
Therefore, to compensate among accuracy, efficiency and storage, the simple MLP
designed based on the nature of FSCK method are developed, i.e., the simple
FSCK MLP (SFM) model, from which those correlated k-values and corresponding
ka-values can be efficiently obtained. Several test cases have been carried out
to compare the developed SFM model and other FSCK tools including look-up
tables and traditional FSCK MLP (TFM) model. Results show that the SFM model
can achieve excellent accuracy that is even better than look-up tables at a
tiny computational cost that is far less than that of TFM model. Considering
accuracy, efficiency and portability, the SFM model is not only an excellent
tool for the prediction of spectral properties, but also provides a method to
reduce the errors due to nonlinear effects.
