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Performance of Hammerstein Spline Adaptive Filtering Based on Fair Cost Function for Denoising Electrocardiogram Signals
(2025-12-01)
Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
This paper proposes a simplified adaptive filtering approach using a Hammerstein function and the spline interpolation based on a Fair cost function for denoising electrocardiogram (ECG) signals. The use of linear filters in real-world applications has many limitations. Adaptive nonlinear filtering is a key development in tackling the challenge of discovering the specific characteristics of biomimetic systems for each person in order to eliminate unwanted signals. A biomimetic system refers to a system that mimics certain biological processes or characteristics of the human body, in this case, the individual features of a person’s cardiac signals (ECG). Here, the adaptive nonlinear filter is designed to cope with ECG variations and remove unwanted noise more effectively. The objective of this paper is to explore an individual biomedical filter based on adaptive nonlinear filtering for denoising the corrupted ECG signal. The Hammerstein spline adaptive filter (HSAF) architecture consists of two structural blocks: a nonlinear block connected to a linear one. In order to make a smooth convergence, the Fair cost function is introduced for convergence enhancement. The affine projection algorithm (APA) based on the Fair cost function is used to denoise the contaminated ECG signals, and also provides fast convergence. The MIT-BIH 12-lead database is used as the source of ECG biomedical signals contaminated by random noises modelled by Cauchy distribution. Experimental results show that the estimation error of the proposed HSAF–APA–Fair algorithm, based on the Fair cost function, can be reduced when compared with the conventional least mean square-based algorithm for denoising ECG signals.
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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.
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Performance analysis of cascade spline adaptive filtering based on normalized orthogonal gradient adaptive algorithm
(2024-12-01)
Wiangtong, Theerayod
;
Sitjongsataporn, Suchada
In this paper, the cascade architecture of spline adaptive filtering (CSAF) for nonlinear systems is presented with the normalized version of orthogonal gradient adaptive (NOGA) algorithm. Spline adaptive filtering comprises a sandwich of the first linear adaptive filtering (LAF) and nonlinear adaptive look-up table. In this cascading architecture, SAF is connected to the second LAF. NOGA is considered as the fast convergence applied by stochastic gradient-based approach. Convergence properties of the proposed NOGA-CSAF algorithm in terms of instantaneous errors can be derived by using Taylor series expansion. Experimental results demonstrate the effectiveness of proposed NOGA-CSAF algorithm using the mean square error scheme. It clearly outperforms the traditional least mean square algorithm on CSAF model in the nonlinear identification system.
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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
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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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Diffusion recursive least squares algorithm based on triangular decomposition
(2023-10-01)
Prongnuch, Sethakarn
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Sitjongsataporn, Suchada
;
Wiangtong, Theerayod
In this paper, diffusion strategies used by QR-decomposition based on recursive least squares algorithm (DQR-RLS) and the sign version of DQR-RLS algorithm (DQR-sRLS) are introduced for distributed networks. In terms of the QR-decomposition method and Cholesky factorization, a modified Kalman vector is given adaptively with the help of unitary rotation that can decrease the complexity from inverse autocorrelation matrix to vector. According to the diffusion strategies, combine-then-adapt (CTA) and adapt-then-combine (ATC) based on DQR-RLS and DQR-sRLS algorithms are proposed with the combination and adaptation steps. To minimize the cost function, diffused versions of CTA-DQR-RLS, ATC-DQR-RLS, CTA-DQR-sRLS and ATC-DiQR-sRLS algorithms are compared. Simulation results depict that the proposed DQR-RLS-based and DQR-sRLS-based algorithms can clearly achieve the better performance than the standard combine-then-adapt-diffusion RLS (CTA-DRLS) and ATC-DRLS mechanisms.
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Diffusion Spline Adaptive Filtering with Adaptive Step-size Normalised Least Mean Square Algorithm
(2023-01-01)
Sitjongsataporn, Suchada
;
Prongnuch, Sethakarn
;
Wiangtong, Theerayod
Diffusion adaptation on spline adaptive filtering (SAF)is presented with combine-then-adapt (CTA) strategy for each node. SAF consists of an adaptive linear filtering and a spline interpolation function. Normalised least mean square algorithm is furnished in the adaptive linear filtering part. An adaptive averaging step-size mechanism is applied for both tap-weight vector of linear and nonlinear filtering parts to provide the fast convergence with low computation complexity. Statistical results testify that the proposed diffusion algorithm is able to provide promising and competitive results to the conventional diffusion strategy algorithm.
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Diffusion Mixed-Node Recursive Least Square-based Algorithm over Adaptive Network
(2022-01-01)
Sitjongsataporn, Suchada
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Prongnuch, Sethakarn
;
Wiangtong, Theerayod
This paper proposes a diffusion framework related on the mixed-node constraint based on recursive least squares (RLS) algorithm with adaptive network. The proposed Combine-Then-Adapt and Adapt-Then-Combine diffusion algorithms based on RLS algorithm are derived shortly by minimising the mixed-node least square-based criterion for distributed estimation over the adaptive network. The mixed-node cost function is described and illustrated briefly. Experimental results depict that these proposed algorithms is able to achieve the fast convergence better than the conventional algorithms.
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Diffusion Affine Projection Sign Least Mean Fourth Algorithm for Distributed Estimation
(2022-01-01)
Sitjongsataporn, Suchada
;
Prongnuch, Sethakarn
;
Wiangtong, Theerayod
The problem of distributed estimation is considered for distributed network. A diffusion model on adaptive affine projection sign algorithm based on least mean fourth algorithm is introduced according to an Adapt-Then-Combine (ATC) design for the distributed framework. Least mean fourth (LMF) algorithm is defined by taking the fourth power of error to against the non-Gaussian environment. An adaptive affine projection sign algorithm based on LMF algorithm is proposed firstly. Then, an ATC diffusion model on adaptive affine projection sign algorithm based on LMF algorithm is presented in terms of the low complexity of sign approach with the order of adaptation and combination, named as ATC-DAPS-LMF. Numerical simulations verify that the proposed algorithm can perform significantly to the conventional affine projection sign 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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