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Iranian Journal of Medical Physics، جلد ۱۲، شماره ۴، صفحات ۲۵۱-۲۶۱
عنوان فارسی
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عنوان انگلیسی A Hybrid Neural Network Approach for Kinematic Modeling of a Novel 6-UPS Parallel Human-Like Mastication Robot
چکیده انگلیسی مقاله Introduction we aimed to introduce a 6-universal-prismatic-spherical (UPS) parallel mechanism for the human jaw motion and theoretically evaluate its kinematic problem. We proposed a strategy to provide a fast and accurate solution to the kinematic problem. The proposed strategy could accelerate the process of solution-finding for the direct kinematic problem by reducing the number of required iterations in order to reach the desired accuracy level. Materials and Methods To overcome the direct kinematic problem, an artificial neural network and third-order Newton-Raphson algorithm were combined to provide an improved hybrid method. In this method, approximate solution was presented for the direct kinematic problem by the neural network. This solution could be considered as the initial guess for the third-order Newton-Raphson algorithm to provide an answer with the desired level of accuracy. Results The results showed that the proposed combination could help find a approximate solution and reduce the execution time for the direct kinematic problem, The results showed that muscular actuations showed periodic behaviors, and the maximum length variation of temporalis muscle was larger than that of masseter and pterygoid muscles. By reducing the processing time for solving the direct kinematic problem, more time could be devoted to control calculations.. In this method, for relatively high levels of accuracy, the number of iterations and computational time decreased by 90% and 34%, respectively, compared to the conventional Newton method. Conclusion The present analysis could allow researchers to characterize and study the mastication process by specifying different chewing patterns (e.g., muscle displacements). 
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نویسندگان مقاله هادی کلانی | hadi kalani
center of excellence on soft computing and intelligent information processing sciip department of mechanical engineering, ferdowsi university of mashhad, mashhad, iran

سازمان اصلی تایید شده: دانشگاه فردوسی (Ferdowsi university)

علیرضا اکبرزاده | alireza akbarzadeh
center of excellence on soft computing and intelligent information processing sciip department of mechanical engineering, ferdowsi university of mashhad, mashhad, iran

سازمان اصلی تایید شده: دانشگاه فردوسی (Ferdowsi university)

سحر مقیمی | sahar moghimi
center of excellence on soft computing and intelligent information processing sciip department of electrical engineering, ferdowsi university of mashhad, mashhad, iran

سازمان اصلی تایید شده: دانشگاه فردوسی (Ferdowsi university)

نشانی اینترنتی http://ijmp.mums.ac.ir/article_6838.html
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