An identification method that uses order overspecified time-series models and a truncated singular value decomposition (SVD) solution is studied. The overspecified model reduces the effects of noise during the identification process, but produces extraneous modes. A backwards approach coupled with a minimum norm approximation, using a truncated SVD solution, enables the system modes to be distinguished from the extraneous modes of the model. Experimental data from a large flexible truss is used to study the effects of varying the truncation of the SVD solution and an order recursive algorithm is used to study the effects of model order. Results show that the SVD may be ineffective in separating the data into signal and noise subspaces. However solutions for highly overspecified model orders exhibit solution properties similar to the minimum norm solution and system and computational modes can be discriminated without a truncated solution.

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