A recursive parameter identification algorithm for nonlinear errors-in-variables models

Key Points

• The algorithm improves parameter identification in nonlinear models, enhancing accuracy.
• Key evidence shows that the recursive approach effectively minimizes errors in variable estimation.
• Observational analysis applies a recursive algorithm tailored for nonlinear errors-in-variables models.
• This method may enable more precise modeling in various applied statistics contexts.

Abstract