A novel online identification method is developed for nonlinear multi-input multi-output process modeling issue, which is based on kernel learning framework and named as online kernel learning (OKL) algorithm in this paper. This proposed approach can adaptively control its complexity and thus acquire controlled generalization ability. The OKL algorithm performs first a forward increasing for incorporating a “new” online sample and then a backward decreasing for pruning an “old” one, both in a recursive manner. Furthermore, the prior knowledge about process can be easily integrated into the OKL scheme to improve its performance. Numerical simulations on a fed-batch penicillin fermentation process show that the proposed OKL algorithm can learn adaptively the dynamics of the process using relatively small samples, indicating the OKL is an attractive online modeling method for fermentation process.
- Kernel Learning,
- Fermentation Processes,
- Online Prediction,
- Recursive Identification
Available at: http://works.bepress.com/inter_liu/2/