Abstract
In this contribution we present a method for modeling a non-stationary process by a combination of fast learning and slowly learning modules, where the fast learning modules transform the input and output data for stable kernel module, which models a situation normalized to be stationary. The proposed method is applied in modeling a non-stationary chemical process.
| Original language | English |
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| Title of host publication | IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE |
| Publisher | IEEE |
| Pages | 199-204 |
| Number of pages | 6 |
| ISBN (Print) | 0-7803-4860-5 |
| Publication status | Published - 1998 |
| MoE publication type | A4 Conference publication |
| Event | IEEE World Congress on Computational Intelligence - Anchorage, United States Duration: 4 May 1998 → 9 May 1998 Conference number: 2 |
Conference
| Conference | IEEE World Congress on Computational Intelligence |
|---|---|
| Abbreviated title | WCCI |
| Country/Territory | United States |
| City | Anchorage |
| Period | 04/05/1998 → 09/05/1998 |