this example uses a limited number of variables for training, it serves as a direct comparison of the performance of linear and non-linear techniques. In Section \[sec-example\] we demonstrate how differences in HI surveys can influence the application of scaling relations. In Section \[sec-performance\] the input data for the ANN are described and also a performance function, R, is introduced for ranking the weights of input parameters in the fitting procedure. A second performance metric is then used to determined which galaxy variables show the most important physical connection with . The third objective is then to develop the non-linear relationships between  and galactic properties into a procedure that can be robustly used to estimate  for galaxies in the SDSS, the results of which are presented in Section \[sec-fitting\]. In Section \[sec-pr\] we describe a pattern recognition procedure that determines how similar a given galaxy is to those that have been used in the training set (i.e. detections in ALFALFA). We hence are able to assign an ALFALFA detection probability, , to any SDSS galaxy; a robust gas fraction can be best determined for galaxies with high detection probabilities (i.e. the galaxy is similar to the ALFALFA detected galaxies on 