Sequential non-linear least-square estimation for damage identification of structures

Drug self-administration experiments are a frequently used approach to assessing the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration’s policy on tobacco regulation, because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

Download Full-text

Square-root algorithms for the continuous-time linear least-square estimation problem

IEEE Transactions on Automatic Control ◽

10.1109/tac.1978.1101862 ◽

1978 ◽

Vol 23 (5) ◽

pp. 907-911 ◽

Cited By ~ 19

Author(s):

M. Morf ◽

B. Levy ◽

T. Kailath

Keyword(s):

Continuous Time ◽

Estimation Problem ◽

Least Square ◽

Square Root ◽

Least Square Estimation ◽

Linear Least Square ◽

Time Linear

Download Full-text

Non-Linear Least Squares Large Misalignment Estimation in Transfer Alignment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.1962 ◽

2014 ◽

Vol 989-994 ◽

pp. 1962-1968 ◽

Cited By ~ 1

Author(s):

Yuan Lu ◽

Xiang Hong Cheng

Keyword(s):

Trust Region Method ◽

Estimation Method ◽

Trust Region ◽

Least Square ◽

Linear Filtering ◽

Linear Least Squares ◽

Transfer Alignment ◽

Non Linear ◽

Simulation Results ◽

Linear Least Square

Large misalignment is unavoidable for subsystems which could be deployed randomly on the carriers such as shipborne aircrafts, AUV. Ordinary linear filtering algorithms don’t converge fast and accurately in non-linear conditions. It's critical for the accuracy of the transfer alignment. In this paper, a new misalignment and gyroscope bias online estimation method based on angular velocity processing is presented. Sensor measurements of M-SINS and S-SINS will be recorded for a certain period. Misalignment and the gyroscope bias will be calculated from these measurements directly with non-linear least square algorithm. Trust region method with pre-conditioning, subspace and conjugate gradient are applied for faster converge and better accuracy. Simulation results demonstrate the effectiveness of the algorithm.

Download Full-text