Static and dynamic complex models: comparison and application to chemical systems

Abstract

PurposeThe purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.Design/methodology/approachApplications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.FindingsThis paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.Research limitations/implicationsThe learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.Practical implicationsThe models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.Originality/valueThe method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.