Influence of low frequency noise in adaptive estimation using the LMS algorithm

Brito, Duarte; Aguiar, Enio; Lucena, Fausto; Freire, Raimundo C. S.; Yasuda, Yutaka; Barros, Allan Kardec

doi:10.1016/j.sigpro.2008.11.003

Cited by 17 publications

(3 citation statements)

References 11 publications

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“…Adicionalmente, se usó el modelo del motor en las coordenadas de flujo orientado del rotor para comprobar la eficiencia del controlador, así como el error cuadrático medio (MSE) para comparar el desempeño entre un controlador PID convencional y el controlador difuso RLS-SM. Los resultados obtenidos se muestran en la Tabla I. Fuente: Basado en el estudio presentado en [37] Es importante anotar que los esquemas adaptativos también han experimentado progresos importantes en el procesamiento de señales derivadas de sistemas complejos como los fisiológicos (señales electrocardiográficas y los potenciales evocados cerebrales, entre otros) y en aplicaciones a sistemas de telecomunicaciones [38]. Sin embargo, las aplicaciones sobre procedimientos autoajustables para estrategias de control de sistemas dinámicos complejos cada vez son más frecuentes, aun cuando las perturbaciones pueden llegar a adoptar naturaleza estocástica [39].…”

Section: Esquemas Adaptativos Para El Control De Sistemas Complejosunclassified

Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control

Gómez¹,

Palacio²,

Valencia³

et al. 2013

Iteckne

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Resumen-El control de procesos con dinámica estocástica o compleja es exitoso siempre y cuando se pueda estimar un modelo que se ajuste bien al comportamiento, sin embargo, esta suposición pierde validez en aplicaciones donde la información del sistema es reducida o incompleta, muy comunes en ambientes reales de la industria. La literatura presenta diferentes esquemas de control, siendo los modelos neuro-difusos los que reportan mejor desempeño. Estos modelos conjugan la capacidad de adaptación que tienen las redes neuronales con la robustez de los motores de inferencia que tiene la lógica difusa, para modelar el conocimiento de expertos mediante reglas de aprendizaje, identificar dinámicas complejas y aumentar la adaptabilidad del sistema a perturbaciones que en la práctica tienden a ser de naturaleza estocástica sumado, a veces, que la información del sistema sea restringida. Este artículo presenta una revisión sobre dificultades y soluciones derivadas del control de sistemas estocásticos o complejos con información incompleta. Se revisan las estructuras de control cuando la dinámica del sistema presenta vaguedad en los datos, la evolución hacia técnicas adaptativas, y el desempeño de las redes neuro-difusas ante procesos estocásticos o complejos con incertidumbre en los datos. De forma preliminar se establece que el control de este tipo de sistemas debe estar compuesto por modelos híbridos soportados en rutinas de optimización y análisis probabilístico que garanticen el tratamiento de las incertidumbres sin afectar el desempeño de las estructuras de control y la consistencia en la precisión.Palabras clave-Control adaptativo, sistemas estocás-ticos, redes neuronales, lógica difusa, modelos neurodifusos, información incompleta.Abstract-Control theory for processes with stochastic or complex dynamics has a successful performance as long as a model can be adjusted to the system behavior, although for real applications in the industry, where data can be reduced or incomplete, this statement may not be true. Different control schemes have been proposed and the literature reports promising results with neural fuzzy models. These models combine the adaptability of neural networks with the robust inference of fuzzy logic, in order to model expert knowledge by learning rules, identify complex dynamics and enhance the control adaptability when stochastic disturbances are present, which sometimes cause incomplete system data. This paper presents a review on the difficulties and solutions related to the control of stochastic or complex systems with incomplete data. This study, initially, discusses the control structures when the system data present notable uncertainty levels. Next, different adaptive control schemes are presented, and finally, nonlinear and stochastic control approaches based on neural fuzzy systems are reviewed. Thereby, in a preliminary way, this review establishes that a system under the conditions mentioned above should be controlled by hybrid models supported on probabilistic routines and optimizatio...

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Section: Esquemas Adaptativos Para El Control De Sistemas Complejosunclassified

Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control

Gómez¹,

Palacio²,

Valencia³

et al. 2013

Iteckne

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show abstract

“…In [23] proposed a noise resilient variable step size LMS which is specifically selected for biomedical applications. Also, various changings are made in literature to improve the performance efficiency of the LMS algorithm [24]. Recently, in [25] proposed various adaptive algorithms which are based on data normalization having less computational capability.…”

Section: Introductionmentioning

confidence: 99%

Comparison of adaptive noise cancelers for ECG signals in wireless biotelemetry system

Khaliq

Waseem

Munir

et al. 2016

2016 International Conference on Intelligent Systems Engineering (ICISE)

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In this paper, least mean square (LMS) adaptive filter and its several variants are used to remove the noise in degraded electrocardiographic (ECG) signals. One of the main interference sources in measurement of ECG signal includes 50 Hz power line that produces distortion in the original signal while measuring. Recently, adaptive filtering is supposed to be one of the most popular and effective methods for the analysis and processing of ECG signals. In this paper comparison has been made and suggests a suitable implementation of the noise canceller for the biotelemetry applications which require high signal to noise (SNR) ratios. These schemes generally involve simple procedures such as shift operations and simple addition and attain effective results as compared to other LMS based realizations.

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“…Denoising methods based on adaptive filters for ECG analysis are widely accepted. However, such algorithms are unable to track the rapidly varying ECG signal [9,10]. L. N. Sharma et al have proposed a denoising method based on an evaluation of higher-order statistics like kurtosis and energy contribution efficiency [11].…”

Section: Introductionmentioning

confidence: 99%

ECG Denoising by Modeling Wavelet Sub-Band Coefficients using Kernel Density Estimation

S.Ardhapurkar¹,

Manthalkar²,

Gajre³

2012

Journal of Information Processing Systems

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Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

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Influence of low frequency noise in adaptive estimation using the LMS algorithm

Cited by 17 publications

References 11 publications

Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control

Dinámica Estocástica o Compleja con Información Incompleta: Una Revisión desde el Control

Comparison of adaptive noise cancelers for ECG signals in wireless biotelemetry system

ECG Denoising by Modeling Wavelet Sub-Band Coefficients using Kernel Density Estimation

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