  We calculate photometric redshifts from the Sloan Digital Sky Survey Data
Release 2 Galaxy Sample using artificial neural networks (ANNs). Different
input patterns based on various parameters (e.g. magnitude, color index, flux
information) are explored and their performances for redshift prediction are
compared. For ANN technique, any parameter may be easily incorporated as input,
but our results indicate that using dereddening magnitude produces photometric
redshift accuracies often better than the Petrosian magnitude or model
magnitude. Similarly, the model magnitude is also superior to Petrosian
magnitude. In addition, ANNs also show better performance when the more
effective parameters increase in the training set. Finally, the method is
tested on a sample of 79, 346 galaxies from the SDSS DR2. When using 19
parameters based on the dereddening magnitude, the rms error in redshift
estimation is sigma(z)=0.020184. The ANN is highly competitive tool when
compared with traditional template-fitting methods where a large and
representative training set is available.
