Determining number of neurons in hidden layers for binary error correcting codes

Mukhtar Hussain; Jatinder S. Bedi; Harpreet Singh

doi:10.1117/12.139978

16 September 1992 Determining number of neurons in hidden layers for binary error correcting codes

Mukhtar Hussain, Jatinder S. Bedi, Harpreet Singh

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139978
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Mukhtar Hussain, Jatinder S. Bedi, and Harpreet Singh "Determining number of neurons in hidden layers for binary error correcting codes", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139978

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