In this paper, it is presented a differential neural network supplied with a new learning law based on the sliding mode approach. The state observer is employed to estimate the dynamics states of degradation mathematical model, where the incomplete information and the limited on-line measure problems are considered. A new training method is applied in the learning algorithm is proposed to reconstruct Biomass, Organic Matter Recalcitrant concentrations and Volume of biological culture evolutions. This allows ensuring an upper bound for the weights time evolution. This new scheme gives the possibility to construct not only one adaptive process but a set of learning laws. The effectiveness of this algorithm is shown by numerical results. © 2006 IEEE.
|Original language||American English|
|Number of pages||4034|
|State||Published - 1 Dec 2006|
|Event||IEEE International Conference on Neural Networks - Conference Proceedings - |
Duration: 1 Dec 2007 → …
|Conference||IEEE International Conference on Neural Networks - Conference Proceedings|
|Period||1/12/07 → …|
Fuentes, R., García, A., Cabrera, A., Poznyak, T., & Chairez, I. (2006). Switching learning law for differential neural observer for biodegradation process. 4484-4490. Paper presented at IEEE International Conference on Neural Networks - Conference Proceedings, .