Wiangtong, Theerayod

Preferred name

Alternative Name

Wiangtong, T.

Main Affiliation

School of Engineering

theerayod.wi@kmitl.ac.th

Now showing 1 - 10 of 22

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Set-membership adaptive reduced-rank affine projection algorithm
(2019-03-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
Set-membership filtering approach with the method of adaptive reduced-rank affine projection algorithm is presented. The distance between the present tap-weight vector and the update is used to accelerate the convergence and decreasing the update rates of proposed algorithm. For the error upper bound constraint, the adaptive averaging threshold parameter is introduced using the estimated auto-correlation between present and previous estimated error vector for controlling the update step-size. Simulation results of proposed algorithm verify the good performance concerning to the amount of updates and convergence rate compared with existing algorithm.
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A heuristic approach for scheduling in heterogeneous distributed embedded systems
(2020-02-01)
Prongnuch, Sethakarn
;
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper presents a heuristic approach for workflow scheduling in heterogeneous distributed embedded system (HDES). A genetic algorithm (GA) and ant colony optimization (ACO) modified with the greedy algorithm introduced to the system contains multiple heterogeneous embedded machines (HEMs) working as a cluster. Users can remotely access and utilize their computational power. The communications on different types of buses are taken into account to find an optimal solution. New meta-heuristic information based on forwarding dependency is proposed to build probability for ACO to generate task priorities. Besides, a greedy algorithm for machine allocation is incorporated to complete task scheduling. Experiments based on random task graphs running in the HEM cluster demonstrate the effectiveness of the modified greedy ant colony optimization algorithm which outperforms the others by 33% more result quality.
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Stochastic behaviour analysis of adaptive averaging step-size sign normalised hammerstein spline adaptive filtering
(2021-01-01)
Wiangtong, Theerayod
;
Prongnuch, Sethakarn
;
Sitjongsataporn, Suchada
We introduce a sign algorithm based on the normalised least mean square with Hammerstein adaptive filtering using adaptive averaging step-size mechanism, which is derived by the minimised absolute a posteriori squared error. To improve the performance by reducing computational complexity, we suggest an adaptive averaging using energy of errors to update step-size variant. The analysis of convergence behaviour and mean square performance are derived. Experimental results reveal that the proposed algorithm can perform better than the least mean square approach based on the Hammerstein model of adaptive filtering.
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An Adaptive inverse Square-root Affine Projection Sign Algorithm based on QR-Decomposition
(2020-10-14)
Sitjongsataporn, Suchada
;
Prongnuch, Sethakarn
;
Wiangtong, Theerayod
In this paper, we present an adaptive averaging step-size inverse square-root affine projection sign algorithm. Based on the QR-decomposition method, we derive the modified inverse autocorrelation matrix in order to reduce the complexity of inverse matrix, which a criterion is based on the proposed algorithm with the sign error. Adaptive averaging step-size mechanism is used for the fast adaptation. Convergence analysis in form of a posteriori error is presented. Simulation results show that the proposed algorithm can obtain clearly the better performance compared with the conventional affine projection algorithm.
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Simplified Set-Membership Affine Projection Least Mean Fourth Algorithm
(2022-01-01)
Sitjongsataporn, Suchada
;
Prongnuch, Sethakarn
;
Wiangtong, Theerayod
A simplified forms of adaptive set-membership affine projection based on least mean fourth algorithm is proposed. Least mean fourth (LMF) algorithm is defined by taking the fourth power of error to against the non-Gaussian environment. A set-membership adaptive filtering is described firstly. Then, an affine projection algorithm is based on LMF algorithm. A simplified set-membership affine projection algorithm based on LMF is introduced. Adaptive threshold parameter and learning rate are presented for improvement of convergence rate. Numerical simulations show that the proposed algorithm can provide significantly to the conventional affine projection algorithm.
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Diffusion Affine Projection Sign Algorithm based on QR-Decomposition
(2021-03-10)
Sitjongsataporn, Suchada
;
Prongnuch, Sethakarn
;
Wiangtong, Theerayod
In this paper, an Adapt-Then-Combine diffusion strategy on affine projection sign algorithm is presented. Based on the QR-decomposition method, the modified inverse autocorrelation matrix is shortly brief to decrease the computational complexity. The Adapt-Then-Combine diffusion strategy is applied on the low complexity of sign algorithm version of inverse square-root affine projection algorithm including with the adaptation and combination steps. By applying an idea of diffusion adaptation algorithm, we derive a diffused version of an affine projection sign algorithm based on QR-Decomposition, denoted as DiQR-APSA. Experimental results show that the proposed DiQR-APSA can achieve the good performance in comparison with the standard affine projection sign algorithm.
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Adaptive Orthogonal Gradient Algorithm Based on Fair Cost Function
(2024-01-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper presents an adaptive orthogonal gradient algorithm with the unconstrained Fair cost function. An adaptive orthogonal gradient-based algorithm is investigated with the help of orthogonal projection mechanism to update the approximate tap-weight vector for the convergence enhancement. Fair cost function is preferable with a smooth points that is able to detect the statistical characteristics of error. Objective of this work is to present an adaptive orthogonal gradient algorithm using Fair cost function (OGA-Fair) to enhance the performance. Simulation results show that proposed adaptive OGA-Fair algorithm can perform with the fast convergence rate and robustness better than the existing method.
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Set-Membership Orthogonal Gradient Adaptive Algorithm on Least Mean Fourth Criterion
(2024-01-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
;
Pookaiyaudom, Panavy
In this paper, we introduce a set-membership mechanism on the orthogonal gradient adaptive (OGA) algorithm based on least mean fourth (LMF) algorithm. In accordance with the fast convergence, the OG A-based scheme is conducted based on LMF algorithm that is described with the fourth power of the estimated error. The organisation of proposed algorithm composes with a brief of set-membership adaptive filtering and taking an advantage of OGA algorithm based on LMF algorithm. In order to make a smooth, an adaptive learning rate of algorithm and threshold value are furnished for convergence rate performance. There are some experimental simulations from proposed algorithm that show the mean square error trends can converge remarkably to the existing algorithm for system identification.
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Spline adaptive filtering based on normalised orthogonal gradient adaptive algorithm
(2019-07-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper proposes a normalised orthogonal gradient adaptive algorithm based on spline adaptive filtering. According to improve the convergence characteristics, the orthogonal gradient adaptive algorithm has been proposed using the orthogonal projection along with the filtered gradient adaptive algorithm. Simulation results demonstrates that the proposed algorithm exhibits more robust performance compared with the conventional spline adaptive filtering algorithms.
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Multitask Diffusion Adaptive Tuning Affine Projection Algorithm for Distributed Model
(2025-01-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper presents combine-then-adapt diffusion model on affine projection adaptive algorithm with adaptive tuning mechanism. An adaptive affine projection algorithm is suggested in the distributed network. Diffusion strategy involves with the combination stage and adaptation stage. By adding the extra-stage in the combination stage, the combination between nodes and inside nodes are built with the global cost function for multitask model to eliminate unknown data. As dealt with fast convergence, the adaptive tuning parameters are assigned in terms of step-size value. Experimental results reveal that this proposed algorithm is able to contribute the approving results for system identifications.

Wiangtong, Theerayod

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