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

Table 5

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 2, 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		4	6	8	10
Proposed approach	L-T(Second)	0.4688	0.7031	0.7969	1.0156
	T-T(Second)	3.0469	4.8125	5.1875	6.5781
G-LSSVR	T-T(Second): 3.2969			L-T (Second): —
M: Training points		49	81	121	169
Local-LSSVR	L-T(Second)	–	–	–	–
	T-T(Second)	107.3438	115.6563	127.4063	155.1563