1. A computerized method for identifying multi-input multi-output Hammerstein models, comprising the steps of: (a) providing an initial set of nonlinear Hammerstein system data acquired from a plant; (b) estimating a set of state-space matrices A, B, C and D from the initial set of nonlinear Hammerstein system data; (c) initializing a swarm of particles with a random population of possible radial basis function neural network weights; (d) calculating a global best set of weights which 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 pre-selected threshold error measure.