Identifica？？o genética de modelos por pólos e zeros baseada no compromisso entre os erros de polariza？？o e variancia

this work proposes a genetic algorithm (ga) to solve process estimation problems when the real process presents high orders polynomials (complexity model) or non-linearities, non-minimum phase behavior, etc. the algorithm finds the best linear model in the pole and zero form to represent the real plant using its input and output signals. a new chromosome representation was introduced and a new ''fitness'' function based on the tradeoff bias x variance was developed. to validate this genetic estimator, simulations studies were done and the ga performance was compared with one obtained by use of the traditional least square estimation method.