Exploring a Local Linear Model Tree Approach to Car-Following

2013 ◽  
Vol 28 (8) ◽  
pp. 581-593 ◽  
Author(s):  
Kayvan Aghabayk ◽  
Nafiseh Forouzideh ◽  
William Young
Keyword(s):  
Linear Model ◽  
Car Following ◽  
Model Tree ◽  
Local Linear ◽  
Tree Approach

Depth estimation of cavities from microgravity data using a new approach: the local linear model tree (LOLIMOT)

2011 ◽  
Vol 10 (3) ◽  
pp. 221-234 ◽  
Author(s):  
A. Hajian ◽  
H. Zomorrodian ◽  
P. Styles ◽  
F. Greco ◽  
C. Lucas
Keyword(s):  
Linear Model ◽  
Depth Estimation ◽  
New Approach ◽  
Model Tree ◽  
Local Linear

scholarly journals Optimized hybrid decoupled visual servoing with supervised learning

2021 ◽  
pp. 095965182110283
Author(s):  
Alireza Rastegarpanah ◽  
Ali Aflakian ◽  
Rustam Stolkin
Keyword(s):  
Linear Model ◽  
Visual Servoing ◽  
Three Dimensional ◽  
Interaction Matrix ◽  
Two Dimensional ◽  
Model Tree ◽  
Local Linear ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Classical Image

This study proposes an optimized hybrid visual servoing approach to overcome the imperfections of classical two-dimensional, three-dimensional and hybrid visual servoing methods. These imperfections are mostly convergence issues, non-optimized trajectories, expensive calculations and singularities. The proposed method provides more efficient optimized trajectories with shorter camera path for the robot than image-based and classical hybrid visual servoing methods. Moreover, it is less likely to lose the object from the camera field of view, and it is more robust to camera calibration than the classical position-based and hybrid visual servoing methods. The drawbacks in two-dimensional visual servoing are mostly related to the camera retreat and rotational motions. To tackle these drawbacks, rotations and translations in Z-axis have been separately controlled from three-dimensional estimation of the visual features. The pseudo-inverse of the proposed interaction matrix is approximated by a neuro-fuzzy neural network called local linear model tree. Using local linear model tree, the controller avoids the singularities and ill-conditioning of the proposed interaction matrix and makes it robust to image noises and camera parameters. The proposed method has been compared with classical image-based, position-based and hybrid visual servoing methods, both in simulation and in the real world using a 7-degree-of-freedom arm robot.

scholarly journals PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Atiye Sarabi-Jamab ◽  
Babak N. Araabi
Keyword(s):  
Linear Model ◽  
Learning Algorithm ◽  
Piecewise Linear ◽  
Learning Algorithms ◽  
Nonlinear Function ◽  
Training Data ◽  
Linear Network ◽  
Data Set ◽  
Model Tree ◽  
Local Linear

Locally linear model tree (LoLiMoT) and piecewise linear network (PLN) learning algorithms are two approaches in local linear neurofuzzy modeling. While both methods belong to the class of growing tree learning algorithms, they use different logics. PLN learning relies on training data, it needs rich training data set and no division test, so it is much faster than LoLiMoT, but it may create adjacent neurons that may lead to singularity in regression matrix. On the other hand, LoLiMoT almost always leads to acceptable output error, but it often needs more rules. In this paper, to exploit the complimentary performance of both algorithms piecewise linear model tree (PiLiMoT) learning algorithm is introduced. In essence, PiLiMoT is a combination of LoLiMoT and PLN learning. The initially proposed algorithm is improved by adding the ability to merge previously divided local linear models, and utilizing a simulated annealing stochastic decision process to select a local model for splitting. Comparing to LoLiMoT and PLN learning, our proposed improved learning algorithm shows the ability to construct models with less number of rules at comparable modeling errors. Algorithms are compared through a case study of nonlinear function approximation. Obtained results demonstrate the advantages of combined modified method.

scholarly journals Local Linear Model Tree with Optimized Structure

2020 ◽  
Vol 53 (2) ◽  
pp. 1163-1168
Author(s):  
Xiaoyan Hu ◽  
Yu Gong ◽  
Dezong Zhao ◽  
Wen Gu
Keyword(s):  
Linear Model ◽  
Model Tree ◽  
Local Linear

scholarly journals Estimation of porosity of rocks based upon axiomatic local linear model tree (a lolimot model) approach

2020 ◽  
Vol 9 (3) ◽  
pp. 785
Author(s):  
Hossein Hossein Iranmanesh ◽  
Ali Mollajan
Keyword(s):  
Linear Model ◽  
Measurement System ◽  
Design Theory ◽  
Axiomatic Design ◽  
Compressional Wave ◽  
Independence Axiom ◽  
Model Tree ◽  
Local Linear ◽  
Before And After ◽  
Axiomatic Design Theory

Shear and Compressional Wave Velocities along with other Petrophysical Logs, are considered as upmost important data for Hydrocarbon reservoirs characterization. In this study, porosity of the extracted rocks form concerned wells is interest as it can indicate the oil capacity of the wells of interest. In this study, we employ the principles of Axiomatic Design theory, specially the first (independence) axiom, to more simplify the measurement system. Also, to clarify the strength of Axiomatic Design theory in reducing the complexity of the system and optimizing the measurement system, we utilize the The Lolimot model (LOcal LInear MOdel Tree) as a model from the neural network family and apply it before and after implementing the basic logic of Axiomatic Design (AD) theory. In addition, in order to illustrate strength of the proposed method emphasizing the effectiveness of a method which benefit from both AD theory and Lolimot model together, the existing system used to measure the rock porosity is addressed and actual data related to one of wells located in southern Iran is utilized. The results of the study show that integrating the Axiomatic Design principles with the LOLIMOT method leads to the least complex and most accurate results.  

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