![]() The proposed CGWO and OBCGWO are then applied to select the relevant features from the original feature set. Initially, several useful features are extracted from the EMG signals to construct the feature set. As for EMG feature selection, the proposed algorithms are evaluated using the EMG data acquired from the publicly access EMG database. The proposed methods show superior results in several benchmark function tests. Moreover, another new variant of CGWO, namely opposition based competitive grey wolf optimizer (OBCGWO), is proposed to enhance the performance of CGWO in feature selection. We model the recently established feature selection method, competitive binary grey wolf optimizer (CBGWO), into a continuous version (CGWO), which enables it to perform the search on continuous search space. This paper proposes a competitive grey wolf optimizer (CGWO) to solve the feature selection problem in electromyography (EMG) pattern recognition.
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