By Topic

Hybrid artificial intelligence methods in oceanographic forecast 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)
Corchado, J.M. ; Dept. de Informatica y Autom., Univ. de Salamanca, Spain ; Aiken, J.

An approach to hybrid artificial intelligence problem solving is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. In situations in which the rules that determine a system are unknown or fuzzy, the prediction of the parameter values that determine the characteristic behavior of the system can be a problematic task. In such a situation, it has been found that a hybrid artificial intelligence model can provide a more effective means of performing such predictions than either connectionist or symbolic techniques used separately. The hybrid forecasting system that has been developed consists of a case-based reasoning system integrated with a radial basis function artificial neural network. The results obtained from experiments in which the system operated in real time in the oceanographic environment, are presented.

Published in:

Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on  (Volume:32 ,  Issue: 4 )