Dynamic functional-link neural networks genetically evolved applied to system identification

The contribution concerns the design of a generalised functional-link neural network with internal dynamics and its applicability to system identification by means of multi-input single output non-linear models of autoregressive with exogenous inputs’ type. An evolutionary search of genetic type and multi-objective optimisation in the Pareto-sense is used to determine the optimal architecture of that dynamic network. The minimised objectives characterise the accuracy of the network and its complexity. Two case studies are included, referring to the identification of an evaporator from a sugar factory, and of a hydraulic looper from a hot rolling mill plant.