Fuzzy Weighted Least Squares Support Vector Regression with Data Reduction for Nonlinear System Modeling

Table 3

Comparison results of the selected different overlap factor for our approach are shown for Example 1, where represents the number of SRMs, L-T the computational time for constructing all SRMs, and the number of data points for each training subset.

The total training data points =501								RMSE	RMSE	L-T
=5								(Training)	(Testing)	(second)
=1.5	128	159	160	160	128	–	–	0.0146	0.0142	0.1875
=2.5	268	183	266	183	267	–	–	0.2086	0.2081	0.3906
=7
=1.5	101	134	134	133	101	133	133	0.0132	0.0129	0.2156
=2.5	147	215	222	222	147	215	223	0.1875	0.1869	0.5103