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  • 标题:Complexity of XOR/XNOR Boolean Functions: A Model using Binary Decision Diagrams and Back Propagation Neural Networks
  • 本地全文:下载
  • 作者:A. Assi ; P.W.C. Prasad ; A. Beg
  • 期刊名称:Journal of Computer Science and Technology
  • 印刷版ISSN:1666-6046
  • 电子版ISSN:1666-6038
  • 出版年度:2007
  • 卷号:7
  • 期号:2
  • 出版社:Iberoamerican Science & Technology Education Consortium
  • 摘要:This paper proposes a model that predicts the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to Binary Decision Diagrams (BDDs). The BPNN model (BPNNM) is developed through the training process of experimental data already obtained for XOR/XNOR-based Boolean functions. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and BPNNM underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the circuit to be implemented. It also proves the computational capabilities of NNs in providing reliable
  • 关键词:Binary Decision Diagrams (BDDs); Reduced ;Ordered Binary Decision diagrams (ROBDDs); ;XOR/XNOR min-terms; Complexity; Boolean Functions
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