Enhanced neural network modelling for a real multivariable chemical process
Application of several Neural Network (NN) modelling techniques to model a Multi-Input Multi-Output (MIMO) nonlinear chemical process is investigated. The process is a laboratory scale chemical reactor with three inputs and three outputs. It typically represents industrial processes due to its nonlinearity, coupling effects and lack of a mathematical model. Different techniques have been used in collecting training data from the reactor. A novel method was used to select the model order and time-delay to determine the NN model input. A Radial Basis Function Network (RBFN) model was then developed. A Recursive Orthogonal Least Squares (ROLS) algorithm was applied as a numerically robust method to update the RBFN weight matrix. In this way, degradation of the modelling error due to ill-conditioning in the training data is avoided. Real data experiments show that the RBFN model developed has high accuracy.
neural network modelling radial basis function networks recursive orthogonal least squares mimo chemical systems nonlinear system identification