Please use this identifier to cite or link to this item: https://repository.unej.ac.id/xmlui/handle/123456789/85192
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dc.contributor.authorAnam, Khairul-
dc.contributor.authorAl-Jumaily, Adel-
dc.date.accessioned2018-04-04T03:08:43Z-
dc.date.available2018-04-04T03:08:43Z-
dc.date.issued2018-04-04-
dc.identifier.issn2088-8708-
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/85192-
dc.descriptionInternational Journal of Electrical and Computer Engineering (IJECE), Vol. 8, No. 1, February 2018, pp. 483~496en_US
dc.description.abstractMyoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and knearest neighbor (kNN).en_US
dc.language.isoenen_US
dc.subjectClassificationen_US
dc.subjectElectromyographyen_US
dc.subjectExtreme learning machineen_US
dc.subjectPattern recognitionen_US
dc.subjectWaveleten_US
dc.titleOptimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognitionen_US
dc.typeArticleen_US
Appears in Collections:LSP-Jurnal Ilmiah Dosen

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