A soft computing approach for modeling of nonlinear dynamical systems

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.