| A Hammerstein Model is composed of a static nonlinear part followed by a linear dynamic part. While identification of single input single output (SISO) hammerstein models has been dealt with efficiently, identification of multi-input multi-output (MIMO) systems is a more complex and difficult issue. In this thesis, identification is carried out by modeling the static nonlinearity with radial basis function neural network (RBFNN), while a state-space model is used to model the linear dynamic part.;Two new algorithms have been proposed in this thesis. The first algorithm makes use of least mean square (LMS) principle for identification of RBFNN weights and subspace identification for identifying state-space models. A second algorithm uses particle swarm optimization (PSO) for estimating the weights of RBFNN and subspace identification for updating the state-space models.;For MIMO systems, update equations have been derived for two distinct cases i.e. when the nonlinearity is separate as well as for the case when the nonlinearity is combined. Simulations have been carried out and proposed algorithms have been validated.;Keywords: Hammerstein, SISO, MIMO, RBFNN, Least Mean Square, Particle Swarm Optimization, State Space Models, Subspace Identification, Static Nonlinearity, Dynamic Linearity... |
