scholarly journals The MLE of the Parameters of a Discrete Competitive System Subject to Environmental Noise

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Huili Xiang ◽  
Zhijun Liu
Keyword(s):  
Numerical Simulation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Environmental Noise ◽  
Competitive System ◽  
Theoretical Results

A randomized discrete competitive system is investigated and the maximum likelihood estimation (MLE) of the parameters of the system is obtained. Also, a corresponding numerical simulation is offered to support our theoretical results.

scholarly journals A Stochastic Model of Stress Evolution in a Bolted Structure in the Presence of a Joint Elastic Piece: Modeling and Parameter Inference

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mohammed Haiek ◽  
Youness El Ansari ◽  
Nabil Ben Said Amrani ◽  
Driss Sarsri
Keyword(s):  
Numerical Simulation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Stochastic Model ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Stress Evolution ◽  
Parameter Inference ◽  
Maximum Likelihood Estimation Method ◽  
Over Time

In this paper, we propose a stochastic model to describe over time the evolution of stress in a bolted mechanical structure depending on different thicknesses of a joint elastic piece. First, the studied structure and the experiment numerical simulation are presented. Next, we validate statistically our proposed stochastic model, and we use the maximum likelihood estimation method based on Euler–Maruyama scheme to estimate the parameters of this model. Thereafter, we use the estimated model to compare the stresses, the peak times, and extinction times for different thicknesses of the elastic piece. Some numerical simulations are carried out to illustrate different results.

Ultrasonic classification and location of 3D room features using maximum likelihood estimation - Part I

Robotica ◽  
1997 ◽  
Vol 15 (5) ◽  
pp. 483-491 ◽  
Author(s):  
Hong Mun-Li ◽  
Lindsay Kleeman
Keyword(s):  
Kalman Filter ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Horizontal Plane ◽  
Sensor Array ◽  
Likelihood Estimation ◽  
Implicit Assumption ◽  
Likelihood Estimator ◽  
Arrival Times ◽  
Theoretical Results

Current mobile robot ultrasonic localisation techniques use sensor systems which rely on features in a horizontal plane. The implicit assumption is that the room boundary on the horizontal plane is not obstructed by objects such as furniture. This assumption is often not realistic and restricts the versatility and portability of these systems. The solution proposed in this paper is the provision of sensing flexibility to use other 3D room boundaries (e.g. ceiling-wall intersections) as 3D natural beacons. We propose a 3D ultrasonic sensor array that uses a Maximum Likelihood Estimator to match the echo arrival times to different object classes and to determine the location of the 3D target. This method does not require fast data acquisition or powerful computing. It has been implemented on a robot localisation application with the Extended Kalman Filter. This paper is the first of two parts, and presents theoretical results on target classification and minimum transducer requirements. The second part, in the next issue of Robotica, presents experimental results on the characterisation of the sensor and its application to robot localisation, and includes the references for the both papers.

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