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.
Highlights
Major revolutions, such as big data, cloud computing, threedimensional printing, and industrial robots, have occurred in information technology and industrial sectors in recent years
Depending on the subject survival mechanism, evolutionary multi-objective optimization algorithms can be divided into algorithms based on the Pareto governing relationship, performance indicators, and decomposition (MOEA/Ds).[15,16,17,18,19]
Under the Multi-objective evolutionary algorithms (MOEAs)/D framework, an algorithm for solving the multi-objective EMG signal recognition problem was designed in this study
Summary
Major revolutions, such as big data, cloud computing, threedimensional printing, and industrial robots, have occurred in information technology and industrial sectors in recent years. Multi-objective evolutionary algorithms (MOEAs) are metaheuristic intelligent optimization approaches simulating the natural evolution process.[13] As one set of Pareto approximate solutions can be obtained in one run alone, this type of algorithm is highly successful in multi-objective optimization field.[14] Depending on the subject survival mechanism, evolutionary multi-objective optimization algorithms can be divided into algorithms based on the Pareto governing relationship, performance indicators, and decomposition (MOEA/Ds).[15,16,17,18,19] MOEA/Ds convert a multi-objective optimization problem into a number of single-objective optimization subproblems. Compared with other types of algorithms, MOEA/Ds have a lower computational complexity and yield a Pareto optimal solution set with better convergence and diversity They have attracted increasing attention among researchers.[20,21,22,23] Under the MOEA/D framework, an algorithm for solving the multi-objective EMG signal recognition problem was designed in this study. The differential evolution algorithm has a more significant approximation effect than the genetic algorithm
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