1. A computerized method for identifying Hammerstein models, comprising the steps of: (a) estimating a set of state-space matrices A, B, C and D from an initial set of nonlinear data acquired from a plant using subspace identification; (b) initializing a swarm of particles with a random population of possible radial basis function neural network weights; (c) calculating a global best set of weights that minimizes an output error measure; (d) estimating a set of radial basis function neural network outputs v based upon the global best set of weights; (e) estimating the state-space matrices A, B, C and D from the radial basis function neural network outputs v estimated in step (d) and a set of original system outputs y; (f) calculating a set of system outputs ŷ from the estimated state-space matrices A, B, C and D of step (e); (g) calculating the output error measure; and (j) repeating steps (b) to (g) if the calculated output error measure is greater than a preselected threshhold error measure.