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Then, the Bayesian optimization method is used to adjust the hyperparameters of our network, which realize the analysis of the joint motor current under different motion states and improve the accuracy of the prediction of joint motion states. Finally, we design the joint current acquisition platform of industrial robot based on Hall current sensors, which can collect joint currents without contact and generate experimental dataset. Comparing with the popular intelligent methods, the results show that our Bayesian optimization framework realizes a more accurate prediction of motion state for the four-axis industrial robot on the basis of contact-less current acquisition.<\/jats:p>","DOI":"10.1007\/s40747-024-01425-z","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T09:01:43Z","timestamp":1712653303000},"page":"4867-4881","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A novel Bayesian optimization prediction framework for four-axis industrial robot joint motion state"],"prefix":"10.1007","volume":"10","author":[{"given":"Li","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hanzhong","family":"Tan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6586-231X","authenticated-orcid":false,"given":"Tao","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"1425_CR1","volume":"77","author":"Z Liu","year":"2022","unstructured":"Liu Z, Liu Q, Xu W, Wang L, Zhou Z (2022) Robot learning towards smart robotic manufacturing: a review. 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