Design and Analysis of Cascaded LMS Adaptive Filters for Noise Cancellation

Dixit, Shubhra; Nagaria, Deepak

doi:10.1007/s00034-016-0332-5

Cited by 28 publications

(14 citation statements)

References 30 publications

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“…The gradient is the del operator that is applied to find the estimate of a function which is the error with respect to the n th coefficient at every instant of time. The concept of cascading adaptive FIR filters is presented in [19] with cascading using LMS adaptive filter.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The secondary noise or the reference noise signal u(n) given to adaptive filter is noise signal n2(n). The output of the adaptive filter y(n), subtracted from the primary signal d(n) gives the desired signal or the error signal e(n) for second adaptive filter [19]. T represents the coefficients of the adaptive FIR filter tap weight vector at time n. Selection of a suitable value for step size parameter µ is imperative to the performance of the adaptive algorithms, i.e.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The two stage structure of ANC as shown in Figure. 2, consist of input signal uc(n) that is cascaded reference input signal vector and cascaded primary signal dc(n) [19].The two stage adaptive algorithms require five distinct steps in each iteration as follows.…”

Section: Analysis Of Two Stage Adaptive Algorithmsmentioning

confidence: 99%

“…Conventional filters,for example, the finite impulse response (FIR) filters [11], infinite impulse response (IIR) filters [12], filter banks [13], polynomial filter [14] and wiener filter are proposed in the literature to limit artifacts. Different methodologies for ECG denoising include adaptive filters, namely the least mean square (LMS) and their variants such as normalized least mean square (NLMS), variable step size least mean square (VSLMS) and variable step size normalized least mean square (VSNLMS) [15,16,17,18,19,20,21,22,23,24,25,26,27]etc.,. A few researchers like wise recommended adaptive kalman filter and extended kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of two stage adaptive FIR Filter Algorithms for PLI and BW artifact cancellation in ECG

Rao¹,

Rao²

2018

IJET

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Electrocardiogram (ECG) is a measure of the electrical movement of the heart, and is obtained by surface electrodes at standardized locations on the patient's chest. During acquisition, various artifacts/noises such as power-line interference (PLI), baseline wander (BW), muscle artifacts (MA) and motion artifacts (EM) obscure the ECG. It is important that these artifacts are minimized for the clinicians to make better diagnosis on heart problems. This paper researches the creative idea of adaptive noise cancellation (ANC) using two stage form of adaptive filters. The concept of cascading and its algorithm for real-time application is simulated on MATLAB. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately which results in good adaptation and performance. The objective of the present investigation is to provide solution in order to improve the performance of noise canceller in terms of filter parameters which are obtained with the help of adaptive algorithms. Different kinds of two stage ANC algorithms are used to eliminate artifacts in ECG by considering the noises such as power line interference and baseline wander. The simulation results show that the performance of the two stage ANC is superior to the conventional single stage ANC system in terms of higher signal-to-noise ratio. Two stage adaptive algorithms are applied on real time ECG signals and compared their performance with the conventional single stage adaptive algorithms in terms of parameters Signal-to-Noise Ratio (SNR), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Distortion.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Section: Analysis Of Two Stage Adaptive Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance analysis of two stage adaptive FIR Filter Algorithms for PLI and BW artifact cancellation in ECG

Rao¹,

Rao²

2018

IJET

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“…Mapping data from an input space into the reproducing kernel Hilbert space (RKHS) through a reproducing kernel [7,8], the nonlinear KAF is obtained in the input space through the linear structure of the RKHS. Recently, the KAF has been widely used in signal processing, such as channel estimation, noise cancellation, and system identification [9][10][11][12][13]. Then, there are some typical nonlinear adaptive filtering algorithms, such as the kernel least mean square (KLMS) algorithm [14], the kernel affine projection algorithm [15], the kernel recursive least square (KRLS) algorithm [16], and many others [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

Luo

Deng

Wang

et al. 2017

Entropy

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Abstract:Recently, inspired by correntropy, kernel risk-sensitive loss (KRSL) has emerged as a novel nonlinear similarity measure defined in kernel space, which achieves a better computing performance. After applying the KRSL to adaptive filtering, the corresponding minimum kernel risk-sensitive loss (MKRSL) algorithm has been developed accordingly. However, MKRSL as a traditional kernel adaptive filter (KAF) method, generates a growing radial basis functional (RBF) network. In response to that limitation, through the use of online vector quantization (VQ) technique, this article proposes a novel KAF algorithm, named quantized MKRSL (QMKRSL) to curb the growth of the RBF network structure. Compared with other quantized methods, e.g., quantized kernel least mean square (QKLMS) and quantized kernel maximum correntropy (QKMC), the efficient performance surface makes QMKRSL converge faster and filter more accurately, while maintaining the robustness to outliers. Moreover, considering that QMKRSL using traditional gradient descent method may fail to make full use of the hidden information between the input and output spaces, we also propose an intensified QMKRSL using a bilateral gradient technique named QMKRSL_BG, in an effort to further improve filtering accuracy. Short-term chaotic time-series prediction experiments are conducted to demonstrate the satisfactory performance of our algorithms.

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Multidimensional Taylor network adaptive control for MIMO time‐varying uncertain nonlinear systems with noises

Zhang

Yan

2019

Intl J Robust & Nonlinear

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Summary In this paper, an adaptive control approach based on the multidimensional Taylor network (MTN) is proposed here for the real‐time tracking control of multiple‐input–multiple‐output (MIMO) time‐varying uncertain nonlinear systems with noises. Two MTNs are used to formulate the optimum control and adaptive filtering approaches. The feed‐forward MTN controller (MTNC) is developed to realize the precise tracking control. The closed‐loop errors between the filtered outputs and expected values are directly chosen as the MTNC's inputs. A valid initial value selection scheme for the weights of the MTNC, which can ensure the initial stability of adaptive process, is introduced. The proposed MTNC can update its weights online according to errors caused by system's uncertain factors, based on stable learning rate. The resilient backpropagation algorithm and the adaptive variable step size algorithm via linear reinforcement are utilized to update the MTNC's weights. The MTN filter (MTNF) is developed to eliminate measurement noises and other stochastic factors. The proposed adaptive MTN filtering system possesses the distinctive properties of the Lyapunov theory–based adaptive filtering system and MTN. Lyapunov function of the filtering errors between the measured values and MTNF's outputs is defined. By properly choosing the weights update law in the Lyapunov sense, the MTNF's outputs can asymptotically converge to the desired signals. The design is independent of the stochastic properties of the input disturbances. Simulation of the MTN‐based control is conducted to test the effectiveness of the presented results.

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Design and Analysis of Cascaded LMS Adaptive Filters for Noise Cancellation

Cited by 28 publications

References 30 publications

Performance analysis of two stage adaptive FIR Filter Algorithms for PLI and BW artifact cancellation in ECG

Performance analysis of two stage adaptive FIR Filter Algorithms for PLI and BW artifact cancellation in ECG

A Quantized Kernel Learning Algorithm Using a Minimum Kernel Risk-Sensitive Loss Criterion and Bilateral Gradient Technique

Multidimensional Taylor network adaptive control for MIMO time‐varying uncertain nonlinear systems with noises

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