Abstract
The minimum information loss (MIL) method for model reduction didn't generate unique result. So the revised minimum information loss (RMIL) method was proposed to solve this problem. By restricting the system model to be the output-normal model and transforming the observability grammian to be an identity matrix, the present RMIL method causes the total information loss to be minimized and preserves the reduced-order model to be unique. Examples are given to illustrate the approximating performance of the reduced-order model derived by RMIL
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