Determining number of neurons in hidden layers for binary error correcting codes
Mukhtar Hussain, Jatinder S. Bedi, Harpreet Singh · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
The determination of number of neurons (H) in hidden layers is very important as it affects the training time and generalization property of neural networks. A higher value of H may force the network to memorize (as opposed to generalize) the patterns which it has seen during training whereas a lower value of H would waste a great deal of training time in finding its optimal representation. It is thus important to devise some methods by which a proper selection of neurons in hidden layers can be made. In this paper, a procedure has been given which determines the number of separable regions (M) in binary error correcting codes (BECC). Thus it is possible to establish link between input training patterns (T), M, and H for such codes without running simulations. Theorems have been developed which provide justification of the use of implied minterm structure (IMS) to BECC. It is shown that BECC are nonlinearly separable (LS) and canonical. Investigations have also been conducted on systematic and nonsystematic codes to prove that systematic codes can be classified with a lesser value of H than the nonsystematic codes as systematic codes require less number of separable regions for their realization.