Title :
Regression tree and neuro-fuzzy approach to system identification of laser lap welding
Author :
Kaur, D. ; Wilson, D. ; Forrest, M. ; Lu, Feng
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Toledo Univ., OH, USA
Abstract :
Laser lap welding quality is a nonlinear response based on a host of material and process variables. This is inclusive of both categorical and numeric variables. An adaptive neuro fuzzy inference system in combination with a classification and regression tree is used to characterize this process. This characterization is shown to be more accurate than that achieved by a nonlinear regression model.
Keywords :
adaptive systems; fuzzy neural nets; fuzzy reasoning; laser beam welding; nonlinear systems; parameter estimation; regression analysis; trees (mathematics); adaptive neurofuzzy inference system; classification tree; laser lap welding quality; nonlinear regression model; regression tree; system identification; Adaptive systems; Classification tree analysis; Computer science; Cost function; Decision trees; Laser theory; Regression tree analysis; Space technology; System identification; Welding;
Conference_Titel :
Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American
Print_ISBN :
0-7803-9187-X
DOI :
10.1109/NAFIPS.2005.1548554
