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

Table 2

Computational time of the proposed method, G-LSSVR and Local-LSSVR with the hyperparameter set , and the overlap factor as 1.5 are shown for Example 1, where the T-T represents total computational time for building the overall process of the proposed method and L-T represents the computational time for constructing all SRMs.

R:the number of SRMs		3	5	7	9	12	15
Proposed approach	L-T(Second)	0.1406	01563	0.1875	0.2344	0.2813	0.2656
	T-T(Second)	0.6406	0.8750	1.2813	1.2969	1.5469	1.7031
G-LSSVR	T-T(Second): 0.2500			L-T(Second): —
M: Training points		21	41	61	81	–	–
Local-LSSVR	L-T(Second)	–	–	–	–	–	–
	T-T(Second)	16.2500	17.8125	17.3125	18.0625	–	–