Table 5

The RMSE and RMSEm values for the best model estimated from artificial data.

Noise

Scenario

DASA

PSO

DE

A717





RMSE

RMSEm

RMSE

RMSEm

RMSE

RMSEm

RMSE

RMSEm


CO

0.0345

0.0345

0.0430

0.0430

0.0064

0.0064

0.6080

0.6080

0%

AO

0.0446

6.0913

0.0406

0.7693

0.0043

1.1807

0.4644

21.3690

TO

0.0468

1.0964

0.0447

0.0877

0.0110

0.1074

0.4542

0.6150

NPO

0.2382

2.5430

0.3198

1.9977

0.1774

12.2287

0.6220

3.0273


CO

0.1064

0.1064

0.1072

0.1072

0.0977

0.0977

0.5363

0.5362

5%

AO

0.0739

0.3343

0.0803

1.8387

0.0678

0.2424

0.3570

0.3723

TO

0.1139

0.1246

0.1096

0.1639

0.0985

0.5058

0.4007

0.9028

NPO

0.2562

3.0161

0.3349

1.4970

0.2163

318.415

0.5189

1.9670


CO

0.3926

0.3926

0.3925

0.3925

0.3904

0.3904

0.6490

0.6490

20%

AO

0.2742

1.3904

0.2735

0.4750

0.2704

1.6916

0.4680

0.6220

TO

0.3948

0.4568

0.3955

0.4368

0.3913

0.3954

0.5698

1.2933

NPO

0.4616

1.8011

0.5055

2.6218

0.4448

5.4207

0.7556

7.2268


The table presents the RMSE and corresponding RMSEm values for the model simulated with the best parameters obtained by parameter estimation with the four optimization methods from artificial data. The best values for RMSE are marked in bold, while the best values for RMSEm are marked in italic.

Tashkova et al. BMC Systems Biology 2011 5:159   doi:10.1186/1752-0509-5-159

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