A novel method of model reduction in linear systems using big bang big crunch optimization and routh pade approximant

A recently developed evolutionary technique Big Bang Big Crunch optimization to derive a reduced order (r
^th
- order) approximant for a stable SISO linear continuous time system. In this method, the error between a set of subsequent time moments/Markov parameters of the original system and that of the model is minimized. The uncertainty existed in choosing the number of time moments and Markov parameter is eliminated. The stability of the model and the first r moments/ Markov parameters is retained.