  System identification is a necessity in control theory. Classical control
theory usually considers processes with integer order transfer functions. Real
processes are usually of fractional order as opposed to the ideal integral
order models. A simple and elegant scheme is presented for approximation of
such a real world fractional order process by an ideal integral order model. A
population of integral order process models is generated and updated by PSO
technique, the fitness function being the sum of squared deviations from the
set of observations obtained from the actual fractional order process. Results
show that the proposed scheme offers a high degree of accuracy.

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