Identification of a class of hyperbolic distributed parameter systems via deterministic learning

In this paper, we investigate a class of hyperbolic distributed parameter systems(DPS), the Sine-Gordon (SG) equation with damp and driven. The plant is a hyperbolic type partial differential equation(PDE) and has been broadly applied for physics and other relevant realms. Instead of identifying the parameters of DPS, the purpose of the paper is to identify the infinite-dimensional dynamics of DPS. By using the method of lines we translate the DPS described by PDE into a set of ODEs, then the dynamics of the DPS are obtained by using the deterministic learning theory and represented by constant radius basis function(RBF) neural network. Furthermore the knowledge also can be used for control or identification.