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This paper investigates the problem of identifying errors-in-variables (EIV) models, where the both input and output measurements are corrupted by white noises, and addresses a new efficient recursive algorithm. The identification problem of EIV models with unknown noise variances has been extensively studied and several methods have been proposed. To be further developed in terms of estimation accuracy, the bias compensated weighted least squares (BCWLS) method with only requirement of inputdoi:10.5687/sss.2019.130 fatcat:osuzy47nlfhhbn7nrpb4r5dak4