A comparison study on a chemical reactor modelling with a physical model and PLRBF networks
The paper presents the modelling practice and considerations for a multivariable process with a first-principle model and three redial basis function (RBF) networks. The process is a laboratory-scaled three-input three-output chemical reactor rig. The RBF networks used are standard RBF networks, pseudo-linear RBF (PLRBF) networks and adaptive PLRBF networks. The first-principle model, the network structures and training algorithms are briefly reviewed. Real data collection with the design of the excitation signal is described. The four models are evaluated by multi-step-ahead prediction errors and the comparison is made. The methods and considerations provide useful experience for deriving data-driven models for industrial processes.
plrbf networks rbf networks process modelling multivariable systems adaptive models