NEURAL NETWORKS WITH COUNTER-PROPAGATION LEARNING-STRATEGY USED FOR MODELING

The neural networks employing the counter-propagation learning strategy are described and their use for making complex models and inverse models is explained. Two examples show how such modelling strategy can yield satisfactory results for the investigated systems. The first example describes building a model for quantitative prediction of the so-called 'colour change' factor. The second example shows the generation of the forward and inverse model for the control of a chemical process in a non-isothermic continuously stirred tank reactor (CSTR).