A bias-compensated total least squares estimator is proposed for models which are linear-in-the-parameters and nonlinear in the perturbed input and output signals. The estimates are obtained using a noniterative and numerically stable method. Strong consistency of both the generalized total least squares and the compensated total least squares estimates are proven. The theoretical properties are verified through simulations.