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 threshold error measure.