Title :
KEDS: a knowledge-based equation discovery system for engineering problems
Author :
Rao, R. Bharat ; Lu, Stephen C -Y
Author_Institution :
Illinois Univ., Urbana, IL, USA
Abstract :
Many engineering phenomena of interest are characterized by non-homogeneity. The authors discuss how the intertwining of the partitioning and discovery processes enables KEDS to learn relationships from engineering data and to extract the structure underlying these relationships. They present the KEDS algorithm and discuss the interaction between the two discovery and partitioning phases. Some extensions to the basic algorithm are described that greatly improve the performance of KEDS and increase the representation power of the models by permitting a probabilistic partitioning of the problem space. The results from running the KEDS system on data from a simulator for an internal combustion engine are presented
Keywords :
combustion; engineering computing; knowledge based systems; learning (artificial intelligence); probabilistic logic; KEDS algorithm; discovery; engineering problems; internal combustion engine; knowledge-based equation discovery system; probabilistic partitioning; simulator; Buildings; Data engineering; Design engineering; Equations; Knowledge based systems; Knowledge engineering; Machine learning; Marine vehicles; Partitioning algorithms; Systems engineering and theory;
Conference_Titel :
Artificial Intelligence for Applications, 1992., Proceedings of the Eighth Conference on
Conference_Location :
Monterey, CA
Print_ISBN :
0-8186-2690-9
DOI :
10.1109/CAIA.1992.200032
