The myoelectric signal is one of the bio-signals which is used by humans to control equipment. To achieve this purpose, a good myoelectric pattern recognition (MPR) is required. The applied classifier and extracted feature set greatly affect the success of M-PR. This paper proposes a hybrid and fast classifier, extreme learning machine (ELM) optimized by improved hybrid particle swarm optimization with wavelet mutation (improved swarm-wavelet). ELM is an improvement of neural network that keeps off repetitive learning to save the training time. In addition to improve the performance of MPR, this paper evaluates the optimization of ELM using improved swarm-wavelet. The swarm-wavelet is improved by using the particle refresh and applying velocity improvement to avoid trapping in local minima. The superiority of the improved swarm-wavelet has been shown in facing up a set of benchmark functions. In ELM, the improved swarm-wavelet is used to find the most suitable parameters to increase the classification accuracy. Furthermore, this paper provides comparisons of improved swarm-wavelet-ELM, swarm-wavelet-ELM and standard swarm-ELM. The experimental results show that the improved swarm-wavelet-ELM is the most accurate classifier with the mean accuracy of 99.6%.

The myoelectric signal is one of the bio-signals which is used by humans to control equipment. To achieve this purpose, a good myoelectric pattern recognition (MPR) is required. The applied classifier and extracted feature set greatly affect the success of M-PR. This paper proposes a hybrid and fast classifier, extreme learning machine (ELM) optimized by improved hybrid particle swarm optimization with wavelet mutation (improved swarm-wavelet). ELM is an improvement of neural network that keeps off repetitive learning to save the training time. In addition to improve the performance of MPR, this paper evaluates the optimization of ELM using improved swarm-wavelet. The swarm-wavelet is improved by using the particle refresh and applying velocity improvement to avoid trapping in local minima. The superiority of the improved swarm-wavelet has been shown in facing up a set of benchmark functions. In ELM, the improved swarm-wavelet is used to find the most suitable parameters to increase the classification accuracy. Furthermore, this paper provides comparisons of improved swarm-wavelet-ELM, swarm-wavelet-ELM and standard swarm-ELM. The experimental results show that the improved swarm-wavelet-ELM is the most accurate classifier with the mean accuracy of 99.6%.

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