Modelling a mimo chemical process using a rbf network with recursive ols updating

摘要

This paper describes neural network modelling techniques for multiinput multi-output (MIMO) non-linear systems. A multi-output radial basis function (RBF) network is employed for process modelling and a MIMO recursive orthogonal least squares (ROLS) algorithm is developed as a numerically robust method to update the weighting matrix in the network. In this way the degradation of the modelling error due to ill-conditioning in the training data is avoided. The modelling techniques are applied to a real MIMO chemical process rig. Real data experiments show that a neural model with high accuracy can be developed for MIMO non-linear processes.