Details

Autor(en) / Beteiligte

• Yang, Ping
• Wu, Chao-peng
• Guo, Yi-lu
• Liu, Hong-bo
• Huang, Hui
• Wang, Hang-zhou
• Zhan, Shu-yue
• Tao, Bang-yi
• Mu, Quan-quan
• Wang, Qiang
• Song, Hong

Titel

Parameter estimation in exponential models by linear and nonlinear fitting methods

Ist Teil von

• Frontiers of information technology & electronic engineering, 2017-03, Vol.18 (3), p.434-444

Ort / Verlag

Hangzhou: Zhejiang University Press

Erscheinungsjahr

2017

Quelle

SpringerLink

Beschreibungen/Notizen

• Estimation of unknown parameters in exponential models by linear and nonlinear fitting methods is discussed. Based on the extreme value theorem and Taylor series expansion, it is proved theoretically that the parameters estimated by the linear fitting method alone cannot minimize the sum of the squared residual errors in the measurement data when measurement noise is involved in the data. Numerical simulation is performed to compare the performance of the linear and nonlinear fitting methods. Simulation results show that the linear method can obtain only a suboptimal estimate of the unknown parameters and that the nonlinear method gives more accurate results. Application of the fitting methods is demonstrated where the water spectral attenuation coefficient is estimated from underwater images and imaging distances, which supports the improvement in the accuracy of parameter estimation by the nonlinear fitting method.