Research Article
An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors
Table 3
The performance of the soft sensor model based on different methods with 10-fold cross validation.
| Method | datasets | RMSE | ARE | Max ARE | training | updating | testing |
| ELM | ~ | S11 | S10 | 0.7987 | 0.0698 | 0.2265 | AdaBoost.R | 0.5915 | 0.0313 | 0.1447 | ILES | 0.3704 | 0.0213 | 0.0689 | ELM | ~ | S11 | S9 | 0.7386 | 0.0868 | 0.3773 | AdaBoost.R | 0.4740 | 0.0394 | 0.1846 | ILES | 0.3309 | 0.0182 | 0.0542 | ELM | ~~ | S11 | S8 | 0.8405 | 0.0772 | 0.2962 | AdaBoost.R | 0.5099 | 0.0295 | 0.1873 | ILES | 0.3323 | 0.0202 | 0.0572 | ELM | ~~ | S11 | S7 | 0.936 | 0.0655 | 0.4255 | AdaBoost.R | 0.4835 | 0.219 | 0.1773 | ILES | 0.3556 | 0.0204 | 0.0525 | ELM | ~~ | S11 | S6 | 0.7342 | 0.0618 | 0.3451 | AdaBoost.R | 0.5198 | 0.0324 | 0.1816 | ILES | 0.3965 | 0.0221 | 0.0641 | ELM | S1~S4,S6~S10 | S11 | S5 | 0.8533 | 0.0872 | 0.3546 | AdaBoost.R | 0.4672 | 0.0336 | 0.1724 | ILES | 0.3531 | 0.0195 | 0.0499 | ELM | ~~ | S11 | S4 | 0.7752 | 0.0749 | 0.3249 | AdaBoost.R | 0.4105 | 0.0362 | 0.1924 | ILES | 0.3934 | 0.0209 | 0.0748 | ELM | ~~ | S11 | S3 | 0.8430 | 0.0823 | 0.3281 | AdaBoost.R | 0.5773 | 0.0525 | 0.1827 | ILES | 0.3656 | 0.0203 | 0.0610 | ELM | ~ | S11 | S2 | 0.9127 | 0.1104 | 0.4302 | AdaBoost.R | 0.5695 | 0.0580 | 0.1891 | ILES | 0.3625 | 0.0211 | 0.0545 | ELM | ~ | S11 | S1 | 0.7905 | 0.0910 | 0.2245 | AdaBoost.R | 0.5045 | 0.0436 | 0.1773 | ILES | 0.3145 | 0.0191 | 0.0756 |
|
|