It is known that the least-squares class of algorithms produce unbiased estimates providing certain assumptions are met. There are many practical problems, however, where the required assumptions are violated. Typical examples include nonlinear dynamical system identification problems, where the input and output observations are affected by measurement uncertainty and possibly correlated noise. This will result in biased least-squares estimates and the identified model will exhibit poor generalization properties. Model estimation for this type of error-in-variables (EIV) problem is investigated in this study and a new identification scheme based on a bootstrap algorithm is proposed to improve the model estimates for nonlinear dynamical system identification.
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