Table 3.

Results on RMSE and RMSEm of the models estimated from artificial data.

Noise	Scenario	RMSE				RMSEm
		DASA	PSO	DE	A717	DASA	PSO	DE	A717
	CO	0.0651	0.0527	0.0189	0.7005	0.0651	0.0527	0.0189	0.7005
0%	AO	0.0625	0.0539	0.0250	0.6099	1.6272	0.7866	1.7876	1.0684
	TO	0.0951	0.1507	0.0197	0.6612	0.5857	0.4606	0.2511	0.7960
	NPO	0.2993	0.5040	0.2282	0.6881	2.6840	2.0717	3.9246	3.0273
	CO	0.1164	0.1121	0.0999	0.7287	0.1164	0.1121	0.0999	0.7287
5%	AO	0.0902	0.0861	0.0690	0.6232	1.0437	0.9043	1.4639	1.3442
	TO	0.1363	0.1341	0.1006	0.6546	0.6162	0.2750	0.2831	0.9265
	NPO	0.3162	0.5166	0.2463	0.6897	2.8668	3.8831	6.6315	2.1172
	CO	0.3958	0.3941	0.3907	0.8113	0.3958	0.3941	0.3907	0.8113
20%	AO	0.2770	0.2760	0.2707	0.6782	1.7547	1.0050	2.8513	1.3052
	TO	0.4023	0.3983	0.3917	0.7810	0.6967	0.4606	0.4289	0.9952
	NPO	0.4929	0.6407	0.4585	0.8023	2.1250	2.5423	2.8333	2.1999

The table presents the median values of RMSE and RMSEm (over the 25 runs) of the models reconstructed with the parameters' estimates obtained by the three optimization methods from artificial data. The best values for both metrics are given in bold.