@article{(Open Science Index):https://publications.waset.org/pdf/11750,
	  title     = {Estimating Shortest Circuit Path Length Complexity},
	  author    = {Azam Beg and  P. W. Chandana Prasad and  S.M.N.A Senenayake},
	  country	= {},
	  institution	= {},
	  abstract     = {When binary decision diagrams are formed from
uniformly distributed Monte Carlo data for a large number of
variables, the complexity of the decision diagrams exhibits a
predictable relationship to the number of variables and minterms. In
the present work, a neural network model has been used to analyze the
pattern of shortest path length for larger number of Monte Carlo data
points. The neural model shows a strong descriptive power for the
ISCAS benchmark data with an RMS error of 0.102 for the shortest
path length complexity. Therefore, the model can be considered as a
method of predicting path length complexities; this is expected to lead
to minimum time complexity of very large-scale integrated circuitries
and related computer-aided design tools that use binary decision
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {9},
	  year      = {2008},
	  pages     = {3146 - 3150},
	  ee        = {https://publications.waset.org/pdf/11750},
	  url   	= {https://publications.waset.org/vol/21},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 21, 2008},