Skip to main content

Table 1 Backpropagation Neural Net Performances.

From: Traffic Flow Condition Classification for Short Sections Using Single Microwave Sensor

   GDM LM SCG
   LogSig-PurLin PurLin-PurLin LogSig-PurLin PurLin-PurLin LogSig-PurLin PurLin-PurLin
BackPrp 3-10-3 Cong 100 100 0.9604 0.9472 100 0.4073
  Dense 0.5733 0.5612 0.5134 0.5671 0 0
  Flow 0.9502 0.9189 0.9210 0.9470 0 0.062
  Total
(Rate/Iteration)
0.9678/1000 0.9258/1000 0.9352/360 0.9170/5 0.0068/156 0.0615/15
BackPrp 3-20-3 Cong 0.6227 0.9472 100 0.9393 100 0.9393
  Dense 0.6914 0.6878 0.4677 0.6778 0.8531 0.6725
  Flow 0.9804 0.8950 0.7492 0.8967 0.9914 0.9468
  Total
(Rate/Iteration)
0.8785/1000 0.9192/1000 0.7367/168 0.9102/4 0.9794/301 0.9512/23
BackPrp 3-50-3 Cong 0.9721 0.9393 0.9732 0.9893 0.9789 0.9472
  Dense 0.4552 0.6686 0.1256 0.6800 0.6318 0.6711
  Flow 0.9571 0.9869 0.9899 0.9907 0.9879 0.9867
  Total
(Rate/Iteration)
0.9120/1000 0.9265/1000 0.9058/35 0.9803/3 0.9595/136 0.9512/16