By Topic

A knowledge-based methodology for tuning analytical models

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)
Freedman, R.S. ; Dept. of Comput. Sci., Polytech. Univ., Brooklyn, NY, USA ; Stuzin, G.J.

A description is presented of a methodology, called knowledge-based tuning, that allows a human analyst and a knowledge-based system to collaborate in adjusting an analytic model. Such a methodology makes the model more acceptable to a decision-maker, and offers the potential for making better decisions than either an analyst or a model can make alone. In knowledge-base tuning, subjective judgments about missing factors are specified by the analyst in terms of linguistic variables. These linguistic variables and knowledge of the model error history are used by the tuning system to infer a specific model adjustment. A logic programming system was developed that illustrates the tuning methodology for a macroeconometric forecasting model. It empirically demonstrates how the predictability of the model can be improved

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:21 ,  Issue: 2 )