A new biased estimation method based on Neumann series for solving ill-posed problems

Abstract

The ill-posed least squares problems often arise in many engineering applications such as machine learning, intelligent navigation algorithms, surveying and mapping adjustment model, and linear regression model. A new biased estimation (BE) method based on Neumann series is proposed in this article to solve the ill-posed problems more effectively. Using Neumann series expansion, the unbiased estimate can be expressed as the sum of infinite items. When all the high-order items are omitted, the proposed method degenerates into the ridge estimation or generalized ridge estimation method, whereas a series of new biased estimates can be acquired by including some high-order items. Using the comparative analysis, the optimal biased estimate can be found out with less computation. The developed theory establishes the essential relationship between BE and unbiased estimation and can unify the existing unbiased and biased estimate formulas. Moreover, the proposed algorithm suits for not only ill-conditioned equations but also rank-defect equations. Numerical results show that the proposed BE method has improved accuracy over the existing robust estimation methods to a certain extent.

Size Effect on Recycled Concrete Strength and Its Prediction Model Using Standard Neutrosophic Number

“…For a particular size ratio r, a unique strength value can be calculated by any one of equations (13) and (14). Generally, the least squares estimate (LSE) [36] is used to obtain the fitting coefficients in equations (13) and (14). However, the ill condition of the equation may lead to serious distortion of the fitting results.…”