Management of graphical symbols in a CAD environment: A neural network approach

Author

Yang, DerShung ; Webster, Julie L. ; Renmdell, L.A. ; Garrett, James H., Jr. ; Shaw, Doris S.

Author_Institution

Dept. of Comput. Sci., Univ. of Illinois at Urbana-Champaign, IL, USA

fYear

1993

fDate

8-11 Nov 1993

Firstpage

272

Lastpage

279

Abstract

A new neural network called AUGURS is designed to assist a user of a computer-aided design package in utilizing standard graphical symbols. AUGURS is similar to the Zipcode Net by Le Cun et al. (1989, 1990) in its encoding of transformation knowledge into its network structure, but is much more compact and efficient. The experiments compare AUGURS with two versions of the Zipcode Net and a traditional layered feedforward network with an unconstrained structure. The experimental results show that AUGURS can recognize a user-drawn symbol with better accuracy and plausibility than the other networks with the least amount of recognition time when the number of training examples is limited

Keywords

CAD; engineering graphics; feedforward neural nets; AUGURS; CAD environment; Zipcode Net; graphical symbols; layered feedforward network; neural network; recognition time; training examples; user-drawn symbol; Application software; Buildings; Computer network management; Design automation; Environmental management; Floppy disks; Intelligent networks; Libraries; Neural networks; Standardization;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1993. TAI '93. Proceedings., Fifth International Conference on

Conference_Location

Boston, MA

ISSN

1063-6730

Print_ISBN

0-8186-4200-9

Type

conf

DOI

10.1109/TAI.1993.633967

Filename

633967