1. A computerized method for identifying Hammerstein models, comprising the steps of: (a) receiving a set of nonlinear system data from a plant; (b) estimating a set of state-space matrices A, B, C and D from the set of nonlinear system data using subspace identification; (c) calculating a set of random synaptic weights and initializing a radial basis function neural network with the set of randomly calculated synaptic weights; (d) calculating an output value ŷ(t); (e) calculating an error value based upon the output value ŷ(t) calculated in step (d) and based upon the estimation of step (b); (f) updating the set of synaptic weights for the radial basis function neural network to train the radial basis function neural network; (g) following training of the radial basis function neural network, calculating intermediate data v; (h) re-estimating the state-space matrices A, B, C and D from the radial basis function neural network outputs v calculated in step (g) and a set of original system outputs y; (i) recalculating a set of system outputs ŷ(t) from the re-estimated state space matrices A, B, C and D of step (f); (j) calculating an output error measure; and (k) repeating steps (c) to (j) if the calculated output error measure is greater than a pre-selected threshold error measure.