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Multichannel optimization for electromyogram signals with complex features in a decomposition-based multi-objective evolution framework with adaptive angle selection
- Source :
- International Journal of Advanced Robotic Systems, Vol 17 (2020), Wang, Z, Chen, G, Li, W, Liu, H & Wang, W 2020, ' Multichannel optimization for electromyogram signals with complex features in a decomposition-based multi-objective evolution framework with adaptive angle selection ', International Journal of Advanced Robotic Systems, vol. 17, no. 2 . https://doi.org/10.1177/1729881420917016
- Publication Year :
- 2020
- Publisher :
- SAGE Publishing, 2020.
-
Abstract
- Intelligent manufacturing is a focus of current manufacturing research, and, in combination with the Internet, it enables accurate real-time control of intelligent equipment. Highly accurate real-time prosthesis control has very important applications in therapeutics, intelligent prosthesis, and other fields. However, the applicability of the current electromyogram signal recognition method is not strong because of multiple factors. These include considering one objective (correctness) only and the inability to consider differences of recognition accuracy between actions, to recognize the number of channels, or to recognize computational complexity. In this article, we propose a multi-objective evolutionary algorithm based on a decomposition-based multi-objective differential evolution framework to construct a multi-objective model for electromyogram signals with multiple features and channels. Such channels and features are balanced and selected by using a support vector machine as an electromyogram signal classifier. Results of substantial experiment analyses indicate that the multi-objective electromyogram signal recognition method is superior to the single-objective ant colony algorithm and that the decomposition-based multiobjective evolutionary algorithms with Angle-based updating and global margin ranking is better than the decomposition-based multi-objective evolutionary algorithm and decomposition-based multiobjective evolutionary algorithms with angle-based updating strategy in handling multi-objective models for electromyogram signals.
- Subjects :
- 0209 industrial biotechnology
Multichannel optimization
Computer science
lcsh:Electronics
lcsh:TK7800-8360
Control engineering
02 engineering and technology
lcsh:QA75.5-76.95
Computer Science Applications
support vector machine classifier
020901 industrial engineering & automation
Artificial Intelligence
surface electromyogram signal
0202 electrical engineering, electronic engineering, information engineering
Decomposition (computer science)
020201 artificial intelligence & image processing
lcsh:Electronic computers. Computer science
Focus (optics)
multi-objective evolution
adaptive angle selection
Software
Selection (genetic algorithm)
Subjects
Details
- Language :
- English
- ISSN :
- 17298814
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- International Journal of Advanced Robotic Systems
- Accession number :
- edsair.doi.dedup.....8d451ddd3b4b367a5bdaef54ff26925e