Online sliding-window methods for process model adaptation
Online learning algorithms are needed when the process to be modeled is time varying or when it is impossible to obtain offline data that cover the whole operating region. To minimize the problems of parameter shadowing and interference, sliding-window-based algorithms are used. It is shown that, by...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2013
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| Texto completo: | http://hdl.handle.net/10400.1/2232 |
| País: | Portugal |
| Oai: | oai:sapientia.ualg.pt:10400.1/2232 |