System identification: theory for the user
System identification: theory for the user
Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Process Modeling,Simulation and Control for Chemical Engineers
Process Modeling,Simulation and Control for Chemical Engineers
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Nonlinear system modeling and robust predictive control based on RBF-ARX model
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Non-linear constrained MPC: Real-time implementation of greenhouse air temperature control
Computers and Electronics in Agriculture
Efficient Nonlinear Predictive Control Based on Structured Neural Models
International Journal of Applied Mathematics and Computer Science
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
Automatica (Journal of IFAC)
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Comparison of four neural net learning methods for dynamic system identification
IEEE Transactions on Neural Networks
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This paper emphasises the link between neural model training and its role in model predictive control (MPC) algorithms. This role is of fundamental importance since in MPC at each sampling instant a model is used on-line to calculate predictions of future behaviour of the process and an optimal future control policy. Taking into account this particular function of models in MPC, a training algorithm of neural dynamic models is derived. An example identification problem of a methanol-water distillation process is discussed. The prediction accuracy of models obtained using the described algorithm and the classical backpropagation scheme is compared, which yields one-step ahead predictors.