Research on the Parameter Estimating Algorithms of Image Edge Detection

2012 ◽  
Vol 562-564 ◽  
pp. 1279-1285
Author(s):  
Ya Ceng Shang ◽  
Jing Chen ◽  
Jun Wei Tian
Keyword(s):  
Linear Model ◽  
Likelihood Estimation ◽  
Least Square Method ◽  
Estimation Algorithm ◽  
Least Square ◽  
Great Likelihood ◽  
Index Model ◽  
Non Linear ◽  
Image Edge ◽  
Future Choice

During detecting the edge of the images, the text partly use great likelihood estimation and least square method estimation algorithm to estimate, we found the result of two estimate algorithms used in the same model are different through experimental analysis. Aiming at above mentioned problems, this text divides the commonly used model in pattern process into the linear model and non-linear model, among the non-linear model, it divides into multinomial model, gauss model, shouldered index model and power counting model, and this text use great likelihood estimate algorithm and least square method estimation algorithm to estimate these models separately, and draw their scope of the application through the experiment, also provide the convenience for the future choice.

scholarly journals COMPARISON OF THE LINEAR AND THE NON-LINEAR BLOCK ADJUSTMENT ON SATELLITE IMAGES RESEARCH

2018 ◽  
Vol XLII-3 ◽  
pp. 775-780
Author(s):  
C. Li ◽  
C. Chen ◽  
Z. Guo ◽  
Q. Liu
Keyword(s):  
Linear Model ◽  
Satellite Images ◽  
Satellite Image ◽  
Least Square Method ◽  
Least Square ◽  
Image Block ◽  
Function Model ◽  
Check Points ◽  
Non Linear ◽  
Linear Least Square

The Rational Function Model （RFM） is a non-linear model. Usually, the RFM-based satellite image block adjustment uses the Taylor series to expand error equations, and then solves the linear model. Theoretically, linearization of a non-linear model affects the accuracy and reliability of the adjustment result. This paper presents linear and non-linear methods for solving the RFM-based block adjustment,and takes ZiYuan3 (ZY-3) satellite imagery block adjustment as an example, using same check points to assess the accuracy of the two methods. In this paper, a non-linear least square method is used for solving the RFM-based block adjustment, which expands a solution to the block adjustment.

scholarly journals Joint clock synchronization and position estimation in time of arrival–based passive positioning systems

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771878689 ◽  
Author(s):  
Shenghong Li ◽  
Lingyun Lu ◽  
Mark Hedley ◽  
David Humphrey ◽  
Iain B Collings
Keyword(s):  
Clock Synchronization ◽  
Likelihood Estimation ◽  
Least Square Method ◽  
Position Estimation ◽  
Least Square ◽  
Time Of Arrival ◽  
Positioning Systems ◽  
Reference Node ◽  
Localization Scheme ◽  
Wireless Signal

A widely used scheme for target localization is to measure the time of arrival of a wireless signal emitted by a tag, which requires the clocks of the anchors (receivers at known locations) to be accurately synchronized. Conventional systems rely on transmissions from a timing reference node at a known location for clock synchronization and therefore are susceptible to reference node failure. In this article, we propose a novel localization scheme which jointly estimates anchor clock offsets and target positions. The system does not require timing reference nodes and is completely passive (non-intrusive). The positioning algorithm is formulated as a maximum likelihood estimation problem, which is solved efficiently using an iterative linear least square method. The Cramér–Rao lower bound of positioning error is also analyzed. It is shown that the performance of the proposed scheme improves with the number of targets in the system and approaches that of a system with perfectly synchronized anchors.

Improved Multiple Point Non-Linear Genetic Algorithm Based Performance Adaptation Using Least Square Method

2011 ◽  
Author(s):  
Y. G. Li ◽  
M. F. Abdul Ghafir ◽  
L. Wang ◽  
R. Singh ◽  
K. Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Engine Performance ◽  
Least Square Method ◽  
Scaling Factor ◽  
Least Square ◽  
Multiple Point ◽  
Search Range ◽  
Non Linear ◽  
Performance Adaptation ◽  
Adaptation Method

At off-design conditions, engine performance model prediction accuracy depends largely on its component characteristic maps. With the absence of actual characteristic maps, performance adaptation needs to be done for good imitations of actual engine performance. A non-linear multiple point Genetic Algorithm based performance adaptation developed earlier by the authors using a set of non-linear scaling factor functions has been proven capable of making accurate performance prediction over a wide range of operating conditions. However, the success depends on searching the right range of scaling factor coefficients heuristically, in order to obtain optimum scaling factor functions. Such search ranges may be difficult to obtain and in many off-design adaption cases, it may be very time consuming due to the nature of trial and error process. In this paper, an improvement on the present adaptation method is presented using a Least Square method where the search range can be selected deterministically. In the new method, off-design adaptation is applied to individual off-design point first to obtain individual off-design point scaling factors. Then plots of the scaling factors against the off-design conditions are generated. Using the Least Square method, the relationship between each scaling factor and the off-design operating condition is generated. The regression coefficients are then used to determine the search range of the scaling factor coefficients before multiple off-design points performance adaptation is finally applied. The developed adaptation approach has been applied to a model single-spool turboshaft engine and demonstrated a simpler and faster way of obtaining the optimal scaling factor coefficients compared with the original off-design adaptation method.

scholarly journals Calculation of ICG kinetics by two simultaneous differential equations and non-linear least square method.

Kanzo ◽  
1988 ◽  
Vol 29 (10) ◽  
pp. 1368-1373
Author(s):  
Yutaka SAGAWA ◽  
Toshiko YOSHIKATA ◽  
Nagaki SHIMADA ◽  
Motonobu SUGIMOTO
Keyword(s):  
Differential Equations ◽  
Least Square Method ◽  
Least Square ◽  
Non Linear ◽  
Linear Least Square

scholarly journals A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3335 ◽  
Author(s):  
Bo Wang ◽  
Muhammad Shahzad ◽  
Xianglin Zhu ◽  
Khalil Ur Rehman ◽  
Saad Uddin
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Linear Model ◽  
Predictive Control ◽  
Least Square ◽  
Support Vector ◽  
Product Concentration ◽  
Grey Wolf Optimization ◽  
Non Linear

l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used as a process model in the non-linear model predictive control to predict product concentration. GWO is also used to solve the non-convex optimization problem in non-linear model predictive control (GWO-NMPC) for calculating optimal future inputs. The proposed GWO-based prediction model (GWO-LSSVM) and non-linear model predictive control (GWO-NMPC) are compared with the Particle Swarm Optimization (PSO)-based prediction model (PSO-LSSVM) and non-linear model predictive control (PSO-NMPC) to validate their effectiveness. The comparative results show that the prediction accuracy, adaptability, real-time tracking ability, overall error and control precision of GWO-based predictive control is better compared to PSO-based predictive control.

Reliability and Usefulness of the Fitting of a Modified Gamma Function to Time-Activity Curves by Means of a Non-Linear Iterative Least-Square Method

1981 ◽  
Vol 20 (04) ◽  
pp. 195-197
Author(s):  
J. M. Fránquiz
Keyword(s):  
Gamma Function ◽  
Least Square Method ◽  
Least Square ◽  
Base Line ◽  
Time Activity ◽  
Appearance Time ◽  
Least Square Fitting ◽  
Fitting Method ◽  
Non Linear ◽  
Least Square Fitting Method

A non-linear iterative least-square fitting method is presented for calculating the parameters of a modified gamma function. The method permits the correction of the appearance time (AT) and the curve parameters in those situations in which AT cannot be estimated with accuracy. The reliability and accuracy of the method is studied in experimental and simulated curves by means of a computer, comparing the results with those obtained by the method of Starmer and Clark for different initial selections of AT and noise at the base line. The usefulness of the method is shown in situations where the curves are distorted in their initial part.

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