Solving Complex Geometry Kinematics Problem with Function Approximation by Artificial Neural Network

Simple item page
dc.contributor.advisorKim, Jin Seob
dc.contributor.advisorKrieger, Axel
dc.creatorGao, Han
dc.creator.orcid0000-0002-7934-6316
dc.date.accessioned2023-02-10T21:14:18Z
dc.date.available2023-02-10T21:14:18Z
dc.date.created2022-12
dc.date.issued2022-12-09
dc.date.submittedDecember 2022
dc.date.updated2023-02-10T21:14:18Z
dc.description.abstractNeural networks have been widely deployed to solve classification and regression problems, and, in recent years, there has been much interest in using neural networks for solving complicated problems. In this paper, a hybrid manipulator kinematics is studied, and an artificial deep neural network is used to solve the inverse kinematics problem. The noise resistance benefit of neural networks approach is explored.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/68050
dc.language.isoen_US
dc.publisherJohns Hopkins University
dc.publisher.countryUSA
dc.subjectRobot Kinematics
dc.subjectArtificial Neural Netwrok
dc.titleSolving Complex Geometry Kinematics Problem with Function Approximation by Artificial Neural Network
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMechanical Engineering
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorJohns Hopkins University
thesis.degree.grantorWhiting School of Engineering
thesis.degree.levelMasters
thesis.degree.nameM.S.E.
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
GAO-THESIS-2022.pdf
Size:
1.67 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.67 KB
Format:
Plain Text
Description:
Download
Collections
ETD -- Graduate theses