Evolving Fuzzy Models for Prosthetic Hand Myoelectric-based Control Using Weighted Recursive Least Squares Algorithm for Identification

Precup, Radu‐Emil; Teban, Teodor-Adrian; Albu, Adriana; Borlea, Alexandra-Bianca; Zamfirache, Iuliu Alexandru; Petriu, Emil M.

doi:10.1109/rose.2019.8790416

Cited by 12 publications

(7 citation statements)

References 29 publications

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“…As future work, we may consider applying other computational intelligent techniques (like type-2 fuzzy logic, convolutional neural network, metaheuristic algorithms and swarm intelligence) that may help in dealing in a more convenient and improved way with this problem. Finally, we envision considering other novel approaches, as the ones outlined in [23,24], and other recent interesting works related to evolutionary or swarm fuzzy models and chaos, as in [25][26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Melín¹,

Castillo²

2021

Preprint

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In this article, the evolution in space and in time of the coronavirus pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries. Self-organizing neural networks possess the capability for clustering countries in the space domain based on their similar characteristics with respect to their coronavirus cases. In this form enabling finding the countries that are having similar behavior and thus can benefit from utilizing the same methods in fighting the virus propagation. To validate the approach, publicly available datasets of coronavirus cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of time series of the countries. Then, a hybrid combination of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient COVID-19 forecasting of the countries. Relevant conclusions have emerged from this study, that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. A lot of the existing works concerned with the Coronavirus have look at the problem mostly from the temporal viewpoint that is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant to improve the forecasting ability. The most relevant contribution of this article is the proposal of combining neural networks with a self-organizing nature for clustering countries with high similarity and the fuzzy fractal approach for being able to forecast the times series and help in planning control actions for the Coronavirus pandemic.

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Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Melín¹,

Castillo²

2021

Preprint

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“…Artificial intelligence methods (such as various kinds of neural networks and fuzzy logic procedures) have been used for nonlinear-based system modeling. For example, applications of fuzzy logic and neural network techniques in system modeling are mentioned in [ 20 , 21 , 22 , 23 , 24 , 25 ], respectively.…”

Section: Related Workmentioning

confidence: 99%

“…To detect anomalies using the model-based technique, modeling is the critical step. System modeling is categorized into two principal groups: (a) mathematical-based system modeling, such as Newton–Euler, Lagrange, and finite element methods, and (b) system identification approaches, such as autoregressive (AR), autoregressive with external input (ARX), ARX–Laguerre techniques, neural network approach, and fuzzy logic methods [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. To improve the accuracy of signal approximation in nonlinear and nonstationary signals, we propose a combination of autoregressive techniques, namely the Laguerre technique with support vector regression (SVR), which will henceforth be called the support vector autoregressive–Laguerre (SVAL).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification

Piltan

Duong

Kim

2021

Sensors

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Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis. Therefore, a deep learning-based adaptive neural-fuzzy structure technique via a support vector autoregressive-Laguerre model is presented in this study. The proposed scheme has three main steps. First, the support vector autoregressive-Laguerre is introduced to approximate the vibration signal under normal conditions and extract the state-space equation. After signal modeling, an adaptive neural-fuzzy structure observer is designed using a combination of high-order variable structure techniques, the support vector autoregressive-Laguerre model, and adaptive neural-fuzzy inference mechanism for normal and abnormal signal estimation. The adaptive neural-fuzzy structure observer is the main part of this work because, based on the difference between signal estimation accuracy, it can be used to identify faults in the bearings. Next, the residual signals are generated, and the signal conditions are detected and identified using a convolution neural network (CNN) algorithm. The effectiveness of the proposed deep learning-based adaptive neural-fuzzy structure technique by support vector autoregressive-Laguerre model was analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed scheme is compared to five state-of-the-art techniques. The proposed algorithm improved the average pattern recognition and crack size identification accuracy by 1.99%, 3.84%, 15.75%, 5.87%, 30.14%, and 35.29% compared to the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of the variable structure technique with the support vector autoregressive-Laguerre model and CNN, the combination of RAW signal and CNN, the combination of the adaptive neural-fuzzy structure technique with the support vector autoregressive-Laguerre model and support vector machine (SVM), the combination of the high-order variable structure technique with the support vector autoregressive-Laguerre model and SVM, and the combination of the variable structure technique with the support vector autoregressive-Laguerre model and SVM, respectively.

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“…The artificial intelligence methods including various kinds of neural networks and fuzzy logic techniques have been recommended for nonlinear system modeling [19][20][21]. Such recent applications pointed out in [22] include the novel interval-valued spherical fuzzy sets for the highly nonlinear system [19], an incremental online identification algorithm to develop a set of evolving fuzzy models (FMs) [20], modeling and classification the nonlinear systems based on artificial neural network and hidden Markov model [21], evolving FMs [22], prediction of system's behavior from the signals by extreme learning machines [23], and the combination of nonlinear autoregressive with exogenous inputs and variable structure recurrent dynamic neural networks [24]. A combination of linear system modeling techniques and intelligent procedures are selected for mathematical modeling of the nonlinear and complex systems.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Bearings Using an Intelligence-Based Autoregressive Learning Lyapunov Algorithm

Piltan¹,

Kim²

2021

IJCIS

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Bearings are complex components with nonlinear behavior that are used to reduce the effect of inertia. They are used in applications such as induction motors and rotating components. Condition monitoring and effective data analysis are important aspects of fault detection and classification in bearings. Thus, an effective and robust hybrid technique for fault detection and identification is presented in this study. The proposed scheme has four main steps. First, a mathematical approach is combined with an autoregressive learning technique to approximate the vibration signal under normal conditions and extract the state-space equation. In the next step, an intelligence-based observer is designed using a combination of the robust Lyapunov-based method, autoregressive learning scheme, fuzzy technique, and adaptive algorithm. The intelligence-based observer is the main part of the algorithm that determines the fault estimation in the bearing. After estimating the signals, in the third step, the residual signals are generated, resampled, and the root mean square (RMS) is extracted from the resampled residual signals. Then, in the final step, the classification, detection, and identification of the signal is performed by the support vector machine algorithm. The effectiveness of the proposed learning control algorithm is analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed method is compared to two state-of-the-art techniques: an autoregressive learning Lyapunovbased observer and a Lyapunov-based observer. The proposed algorithm improved the average fault identification accuracy by 3.9% and 5.2% compared to the autoregressive learning Lyapunov-based approach and the Lyapunov-based technique, respectively.

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Evolving Fuzzy Models for Prosthetic Hand Myoelectric-based Control Using Weighted Recursive Least Squares Algorithm for Identification

Cited by 12 publications

References 29 publications

Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Spatial and Temporal Spread of the Coronavirus Pandemic using Self Organizing Neural Networks and a Fuzzy Fractal Approach

Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification

Fault Diagnosis of Bearings Using an Intelligence-Based Autoregressive Learning Lyapunov Algorithm

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