LEAST-SQUARES MODEL-REDUCTION

A model reduction method based on the least squares algorithm is deriv ed. This method calculates a low order autoregressive moving average ( ARMA) predictor equation from a high order ARMA equation. The low orde r ARMA equation minimizes the sum of the squares of the prediction err ors when the input is white noise. This is almost equivalent to minimi zing the sum of the squares of the error in the impulse response funct ion. Transfer function models can also be used and a steady-state gain constraint can be incorporated into the procedure. The merits of this method of model order reduction are shown with three examples. In the se examples, the proposed method produced results that compared favora bly with highly regarded existing model reduction techniques which req uire many more computations.