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

Table 8

Comparison results of the proposed method, [15], G-LSSVR and Local-LSSVR with the hyper-parameter set , and the overlap factor as 1.5 are shown for Example 3.

R:the number of SRMs		3	5	7	9
Proposed approach	L-T(Second)	0.2500	0.2813	0.2656	0.3594
	T-T(Second)	0.3281	1.0938	1.3438	1.5313
G-LSSVR	T-T(Second): 0.1875			L-T (Second): —
M: Training points		49	81	121	169
Local-LSSVR	L-T(Second)	–	–	–	–
	T-T(Second)	16.5469	16.7813	17.9063	18.1719