Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

Table 2

MAPE 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)	5.8327	0.6123	0.5415	1.0993	0.7288	—
	ReOS-ELM	—	—	—	—	—	0.3236
	DR-ELM	0.3412	0.4318	0.3440	0.7437	0.4007	—
	FORELM (𝑝=2)	0.2689	0.3639	0.3135	0.8583	0.4661	—
𝐿=25	FOS-ELM	5.0473	1.7010	2.4941	0.9537	0.9272	—
	ReOS-ELM	—	—	—	—	—	0.4773
	DR-ELM	0.3418	0.3158	0.4577	0.5680	0.3127	—
	FORELM	0.3391	0.2568	0.3834	0.6228	0.2908	—
𝐿=50	FOS-ELM	×	×	×	209.8850	3.9992	—
	ReOS-ELM	—	—	—	—	—	0.2551
	DR-ELM	0.3876	0.3109	0.3808	0.5486	0.3029	—
	FORELM	0.3478	0.2960	0.3145	0.5504	0.3514	—
𝐿=100	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.2622
	DR-ELM	0.2473	0.2748	0.2163	0.5471	0.3381	—
	FORELM	0.3229	0.2506	0.2074	0.5501	0.2936	—
𝐿=150	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.3360
	DR-ELM	0.2725	0.2365	0.2133	0.5439	0.3071	—
	FORELM	0.2713	0.2418	0.2050	0.5432	0.3133	—
𝐿=200	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.2687
	DR-ELM	0.2539	0.2454	0.2069	0.5443	0.3161	—
	FORELM	0.2643	0.2370	0.2029	0.5443	0.3148	—
𝐿=400	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.2657
	DR-ELM	0.2520	0.2456	0.2053	0.5420	0.3260	—
	FORELM	0.2277	0.2382	0.2285	0.5430	0.3208	—
𝐿=800	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.3784
	DR-ELM	0.2388	0.2401	0.3112	0.5415	0.3225	—
	FORELM	0.2128	0.2325	0.3006	0.5416	0.3247	—
𝐿=1000	FOS-ELM	×	×	×	×	×	—
	ReOS-ELM	—	—	—	—	—	0.4179
	DR-ELM	0.2230	0.2550	0.3412	0.5431	0.3283	—
	FORELM	0.2228	0.2525	0.3347	0.5412	0.3293	—
FOKELM		0.2397	0.2345	0.3125	0.5421	0.3178	—