@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 diagrams.}, 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}, }