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For model identification of industrial operating systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem, a subspace identification method is proposed in this paper by developing an orthogonal projection approach to guarantee consistent estimation of the deterministic part of such a system. The rank condition for such orthogonal projection is analyzed in terms of the nominal state-space model structure. Using the principal component analysis (PCA), thedoi:10.3182/20120710-4-sg-2026.00141 fatcat:2iym2riahrhkfmw2k7w6mf3qlm