Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Original Contribution: Stacked generalization
Neural Networks
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Machine Learning
Model selection in neural networks
Neural Networks
Applications of Neural Networks and Other Learning Technologies in Process Engineering
Applications of Neural Networks and Other Learning Technologies in Process Engineering
Recurrent neuro-fuzzy networks for nonlinear process modeling
IEEE Transactions on Neural Networks
Improving model accuracy using optimal linear combinations of trained neural networks
IEEE Transactions on Neural Networks
Choosing grid points in solving singular optimal control problems by iterative dynamic programming
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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The performance of empirical model based fed-batch process optimal control is strongly affected by the model prediction reliability at the end-point of a batch. An optimal control profile calculated from an empirical model may not give the best performance when applied to the actual process due to model-plant mismatches. To tackle this issue, a new method for improving the reliability of fed-batch process optimal control by incorporating model prediction confidence bounds is proposed. Multiple neural networks (MNN) are used to build an empirical model of fed-batch process based on process operation data. Model prediction confidence bounds are calculated based on predictions of all component networks in an MNN model and the model prediction confidence bound at the end-point of a batch is incorporated into the optimization objective function. The modified objective function penalizes wide prediction confidence bounds in order to obtain a reliable optimal control profile. The non-linear optimization problem based on MNN with augmented objective function is solved by iterative dynamic programming. The proposed control strategy is illustrated on a simulated fed-batch ethanol fermentation process. The results demonstrate that the optimal control profile calculated from the proposed approach is reliable in the sense that its performance degradation is limited when applied to the actual process.