Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

Table 3

Running time (second) comparison between the proposed ELMs and other ELMs on identification of process (20) with input (23).

𝑧		50	70	100	150	200	∞
𝐿=15	FOS-ELM (𝑝=2)	0.0620	0.0620	0.0621	0.0621	0.0622	—
	ReOS-ELM	—	—	—	—	—	0.0160
	DR-ELM	0.2650	0.3590	0.5781	1.0790	1.6102	—
	FORELM (𝑝=2)	0.0620	0.0620	0.0620	0.0621	0.0622	—
𝐿=25	FOS-ELM	0.0621	0.0621	0.0621	0.0623	0.0623	—
	ReOS-ELM	—	—	—	—	—	0.0167
	DR-ELM	0.2500	0.3750	0.6094	1.0630	1.6250	—
	FORELM	0.0621	0.0621	0.0621	0.0622	0.0622	—
𝐿=50	FOS-ELM	×	×	×	0.1250	0.1250	—
	ReOS-ELM	—	—	—	—	—	0.0470
	DR-ELM	0.2650	0.3906	0.6250	1.1100	1.6560	—
	FORELM	0.1400	0.1562	0.1560	0.1250	0.1250	—
𝐿=100	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.1250
	DR-ELM	0.2813	0.4219	0.6719	1.2031	1.7380	—
	FORELM	0.3750	0.4063	0.3440	0.3440	0.3280	—
𝐿=150	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.2344
	DR-ELM	0.3125	0.4540	0.7500	1.2970	1.9220	—
	FORELM	0.7810	0.8003	0.6560	0.6720	0.6570	—
𝐿=200	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.3901
	DR-ELM	0.3290	0.5160	0.7810	1.3440	1.9690	—
	FORELM	1.2702	1.2750	1.1250	1.0320	0.9375	—
𝐿=400	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	1.7350
	DR-ELM	0.3906	0.5781	0.8750	1.4680	2.2820	—
	FORELM	6.6410	6.4530	6.2030	5.6090	5.1719	—
𝐿=800	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	8.7190
	DR-ELM	0.5470	0.7970	1.2660	2.0938	2.9840	—
	FORELM	32.7820	31.6720	30.1400	27.5780	27.7500	—
𝐿=1000	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	13.6094
	DR-ELM	0.6250	1.0000	1.4530	2.4380	3.4060	—
	FORELM	51.4530	49.3440	46.8750	43.1880	38.9060	—
FOKELM		0.2811	0.4364	0.6951	1.1720	1.7813	—