Title :
A 3D transformation of a rigid link system using back propagation
Author :
Wells, D.M. ; Vaughan, C.L.
Author_Institution :
Bioeng. Dept., Clemson Univ., SC, USA
Abstract :
Summary form only given. In this study, a rigid link system with three degrees of freedom was used to assess the ability of a neural net to generalize a transformation between the xyz end-point coordinates of the rigid body and the corresponding joint angles theta /sub 1/, theta /sub 2/ and theta /sub 3/. For this application, distortion can be quantified as a radial displacement between the expected and observed output patterns. A series of training point densities, within the input pattern domain, was used to characterize the interpolative properties of the net. A minimum averaged error term of 3.5 parts per thousand was calculated using a net with 32 middle layer units.<>
Keywords :
learning systems; neural nets; pattern recognition; 3D transformation; back propagation; input pattern domain; interpolative properties; joint angles; minimum averaged error term; neural net; output patterns; radial displacement; rigid link system; training point densities; xyz end-point coordinates; Learning systems; Neural networks; Pattern recognition;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118539
