EURASIP Journal on Advances in Signal Processing

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

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