Anzeige
Erschienen in:
2017 | OriginalPaper | Buchkapitel
A Soft Computing Approach for Modeling of Nonlinear Dynamical Systems
verfasst von : Rajesh Kumar, Smriti Srivastava, J. R. P. Gupta
Erschienen in: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications
Verlag: Springer Singapore
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
A procedure based on the use of radial basis function network (RBFN) is presented for black box modeling of nonlinear dynamical systems. The generalization ability of RBFN is invoked to approximate the mathematical model of the given unknown nonlinear plant. This approximate model will then be used to predict the output of the plant at any given time instant. The parameters associated with RBFN are updated using the recursive equations obtained through the gradient-descent principle. The other benefit of using gradient descent principle is that it exhibits the clustering effect while adjusting the radial centers of RBFN. Real-time data of two benchmark problems: Box-Jenkins gas furnace data and Chemical process (polymer production), were used to show the application of RBFN for modeling purpose. Simulation results show that RBFN is well suited as a modeling tool for capturing the unknown nonlinear dynamics of the plant.