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Research on algorithms for control design of human–machine interface system using ML

Research on algorithms for control design of human–machine interface system using ML In recent years, progressive attention has been paid to the research of man–machine interface. In many fields of application software system development, experts have listed man–machine interface as one of the commands for the urgent research and advancement. This article has proposed the improvement to the man–machine interface system control design for the practical value of life, and has presented an approach for algorithms using Machine Learning. The method used in the paper includes the spatial layout of human–machine interface supported by the design cognition model and the use of Machine Learning algorithms. Based on Max–Min Ant System, the optimal path of ant construction is obtained, which is the optimal layout scheme. The experimental result in the paper shows that: with the support of the cognitive model, the cabin man–machine interface system control design method based on Genetic Algorithm-Ants Algorithm is obtained. The layout design principles were summarized by the cognitive model, and the layout optimization objective function was constructed according to each principle, and the problem of solving layout parties and cases was transformed into a combinatorial optimization problem. The form of fitness function, pheromone, and heuristic information for layout optimization was studied, and the algorithm of manual optimization process was implemented based on Genetic Algorithm and Ant Algorithm, in order to obtain a good optimization performance and time performance as a result. The practical value of the control design of man–machine interface system with the Machine Learning algorithm is proved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of System Assurance Engineering and Management Springer Journals

Research on algorithms for control design of human–machine interface system using ML

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References (28)

Publisher
Springer Journals
Copyright
Copyright © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2021
ISSN
0975-6809
eISSN
0976-4348
DOI
10.1007/s13198-021-01469-1
Publisher site
See Article on Publisher Site

Abstract

In recent years, progressive attention has been paid to the research of man–machine interface. In many fields of application software system development, experts have listed man–machine interface as one of the commands for the urgent research and advancement. This article has proposed the improvement to the man–machine interface system control design for the practical value of life, and has presented an approach for algorithms using Machine Learning. The method used in the paper includes the spatial layout of human–machine interface supported by the design cognition model and the use of Machine Learning algorithms. Based on Max–Min Ant System, the optimal path of ant construction is obtained, which is the optimal layout scheme. The experimental result in the paper shows that: with the support of the cognitive model, the cabin man–machine interface system control design method based on Genetic Algorithm-Ants Algorithm is obtained. The layout design principles were summarized by the cognitive model, and the layout optimization objective function was constructed according to each principle, and the problem of solving layout parties and cases was transformed into a combinatorial optimization problem. The form of fitness function, pheromone, and heuristic information for layout optimization was studied, and the algorithm of manual optimization process was implemented based on Genetic Algorithm and Ant Algorithm, in order to obtain a good optimization performance and time performance as a result. The practical value of the control design of man–machine interface system with the Machine Learning algorithm is proved.

Journal

International Journal of System Assurance Engineering and Management – Springer Journals

Published: Mar 1, 2022

Keywords: Man–machine interface (M-MI); Cognitive model; Ant construction; Machine learning algorithm; Max–min ant system (MMAS); Ant colony optimization (ACO); Meta heuristic approach

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