An Algorithm for Optimally Fitting a Wiener Model

Table 2

Training and validation statistics for Wiener networks fit to model blood glucose concentrations for four diabetic subjects. Note that PM is the proposed methodology, GN is the modified Gauss-Newton algorithm, LM is the modified Levenberg-Marquardt algorithm, and ES is the Excel Solver methodology. Note that ES was fit manually.

Subject	Algorithm	AAETr (mg/dL)	𝑟fit	𝑟Val	Time (s)
1	PM	12.4	0.60	0.59	4127
	GN	12.5	0.40	0.54	347
	LM	12.5	0.45	0.56	83
	ES	7.2	0.83	0.52	—
2	PM	6.8	0.84	0.56	10592
	GN	9.0	0.77	0.43	535
	LM	6.2	0.86	0.58	97
	ES	6.9	0.84	0.49	—
3	PM	11.5	0.71	0.52	4735
	GN	6.8	0.81	0.58	793
	LM	7.2	0.80	0.55	96
	ES	7.8	0.75	0.48	—
4	PM	11.4	0.82	0.68	7032
	GN	11.8	0.81	0.60	1028
	LM	11.7	0.81	0.59	79
	ES	13.3	0.72	0.51	—