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