将随机游走法和层次法相结合,采用层次化随机游走法对静态P/G网(Power and Ground Networks)进行分析.针对大规模的电路,在通过多层的参数提取和建模得到静态P/G网模型后,运用层次法将P/G网分割,在子网内采用随机游走法,并且在此基础上...将随机游走法和层次法相结合,采用层次化随机游走法对静态P/G网(Power and Ground Networks)进行分析.针对大规模的电路,在通过多层的参数提取和建模得到静态P/G网模型后,运用层次法将P/G网分割,在子网内采用随机游走法,并且在此基础上比较5种加速算法.实验数据表明,改进的双共轭梯度(BCG)随机游走法的计算速度是普通随机游走法的6倍以及是层次法的14倍.新方法有效地节省了计算时间,有益于对P/G网的研究.展开更多
The noniterative algorithm of multiscale MRF has much lower computing complexity and better result thanits iterative counterpart of noncausal MRF model, since it has causality property between scales, and such causali...The noniterative algorithm of multiscale MRF has much lower computing complexity and better result thanits iterative counterpart of noncausal MRF model, since it has causality property between scales, and such causality isconsistent with the character of images. Maximizer of the posterior marginals(MPM)algorithm of multiscale MRFmodel is presented for only one image can be obtained in image segmentation. EM algorithm for parameter estimate isalso given. Experiments demonstrate that comparing with iterative ones, the proposed algorithms have the character-istics of greatly reduced computing time and better segmentation results. This is more notable for large images.展开更多
文摘将随机游走法和层次法相结合,采用层次化随机游走法对静态P/G网(Power and Ground Networks)进行分析.针对大规模的电路,在通过多层的参数提取和建模得到静态P/G网模型后,运用层次法将P/G网分割,在子网内采用随机游走法,并且在此基础上比较5种加速算法.实验数据表明,改进的双共轭梯度(BCG)随机游走法的计算速度是普通随机游走法的6倍以及是层次法的14倍.新方法有效地节省了计算时间,有益于对P/G网的研究.
文摘The noniterative algorithm of multiscale MRF has much lower computing complexity and better result thanits iterative counterpart of noncausal MRF model, since it has causality property between scales, and such causality isconsistent with the character of images. Maximizer of the posterior marginals(MPM)algorithm of multiscale MRFmodel is presented for only one image can be obtained in image segmentation. EM algorithm for parameter estimate isalso given. Experiments demonstrate that comparing with iterative ones, the proposed algorithms have the character-istics of greatly reduced computing time and better segmentation results. This is more notable for large images.
