By Topic

A knowledge-based equation discovery system for engineering domains

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Roa, R.B. ; Siemens Corp. Res., Princeton, NJ, USA ; Lu, S.C.-Y.

KEDS (knowledge-based equation discovery system), which integrates machine-learning and statistical techniques to learn a range of comprehensible models in nonhomogeneous domains, is described. The intertwining of partitioning and discovery enables it to learn relationships from data and extract their underlying structure, and probabilistic clustering enhances runtime performance and improves accuracy. KEDS uses a divide-and-conquer strategy, breaking the engineering problem space into smaller regions so as to meet two goals: partitioning should make it easier to discover the models in each region, and he resulting model for each region should meet the comprehensibility requirements imposed by the engineer. KEDS is also a recursive fit-and-split system, which finds partial hypotheses (candidate equations), and then partitions the problem space into regions.<>

Published in:

IEEE Expert  (Volume:8 ,  Issue: 4 )