Abstract
The accurate and reliable online measurement of the product yield is very essential for the control and optimization of the biodiesel process. A biodiesel yield prediction model based on the fast decorrelated neural network ensembles (FDNNE) was established to enhance the estimated performance. The random vector functional link (RVFL) networks were inserted into the fast decorrelated neural network ensemble frame as the base model since it could provide better generalized performance and faster speed. The FDNNE product yield prediction model initializes the hidden layer parameters of base models randomly, and calculates the output layer parameters using the least square method with negative correlation learning. Simulation results show that the proposed method has relatively higher accuracy and reliability compared with the single RVFL model.
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