Multichannel parallelizable sliding window RLS and fast RLS algorithms with linear constraints

Djigan, Victor I.

doi:10.1016/j.sigpro.2005.06.010

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…We can notice a good correlation between the real muscle force and the response of the model. Parameters estimation of the H–W sub-models is based on the recursive least squares algorithm [ 57 , 58 ]. We note that the actual force is a continuous blue line and the estimated force is the dotted red line.…”

Section: Methodsmentioning

confidence: 99%

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

2022

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This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.

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Section: Methodsmentioning

confidence: 99%

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

2022

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“…The implementation of the RLS algorithm is optimised by exploiting the inversion matrix lemma and provides fast convergence and small error rates (Djigan 2006).…”

Section: Adaptive Rls Filteringmentioning

confidence: 99%

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

Hao¹,

Zhao²,

Chen³

et al. 2009

Czech J. Food Sci.

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Lin H., Zhao J., Chen Q., Cai J., Zhou P. (2009): Eggshell crack detection based on acoustic impulse response and supervised pattern recognition. Czech J. Food Sci., 27: 393-402.A system based on acoustic resonance was developed for eggshell crack detection. It was achieved by the analysis of the measured frequency response of eggshell excited with a light mechanism. The response signal was processed by recursive least squares adaptive filter, which resulted in the signal-to-noise ratio of the acoustic impulse response reing remarkably enhanced. Five features variables were exacted from the response frequency signals. To develop a robust discrimination model, three pattern recognition algorithms (i.e. K-nearest neighbours, artificial neural network, and support vector machine) were examined comparatively in this work. Some parameters of the model were optimised by cross-validation in the building model. The experimental results showed that the performance of the support vector machine model is the best in comparison to k-nearest neighbours and artificial neural network models. The optimal support vector machine model was obtained with the identification rates of 95.1% in the calibration set, and 97.1% in the prediction set, respectively. Based on the results, it was concluded that the acoustic resonance system combined with the supervised pattern recognition has a significant potential for the cracked eggs detection.

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“…(20) is linked to the matrix R k in the algorithm. The main difference between this proposed algorithm and that in ref.…”

Section: Remarkmentioning

confidence: 99%

“…In the model selection, beside a threshold for E k < δ presented in refs. [19,20], it is necessary to employ the additional stop criterion δE k =E k+1 − E k < δ, because E k is leveled off at some point due to the output noise [21] .…”

Section: Remarkmentioning

confidence: 99%

Simple recursive algorithm for linear-in-theparameters nonlinear model identification

Jin

2009

Sci. China Ser. F-Inf. Sci.

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This paper introduces a simple recursive algorithm for nonlinear dynamic system identification using linear-in-the-parameters models for NARX or RBF network where both the structure and parameters can be obtained simultaneously and recursively. The main objective is to improve the numerical stability when the model terms are highly correlated. This is based on the "innovation" idea and net contribution criteria. Using the recursive formulae for the computation of the Moore-Penrose inverse of matrices and the net contribution of model terms, it is possible to combine the structure term determination and parameters estimation within one framework by adding and deleting an item in the selected candidate model. The formulae for enhancing and reducing a matrix are given. Simulation results show the proposed method is numerically more stable than existing approaches.nonlinear dynamic system, linear-in-the-parameters models, NARX, RBF, Moore-Penrose inverse

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Multichannel parallelizable sliding window RLS and fast RLS algorithms with linear constraints

Cited by 13 publications

References 15 publications

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Eggshell crack detection based on acoustic impulse response and supervised pattern recognition

Simple recursive algorithm for linear-in-theparameters nonlinear model identification

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