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EMG Pattern Classification by Split and Merge Deep Belief Network.
- Source :
- Symmetry (20738994); Dec2016, Vol. 8 Issue 12, p148, 11p
- Publication Year :
- 2016
-
Abstract
- In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly--even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20738994
- Volume :
- 8
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- Symmetry (20738994)
- Publication Type :
- Academic Journal
- Accession number :
- 120298818
- Full Text :
- https://doi.org/10.3390/sym8120148