By Topic

Issues in designing automated minimal resource allocation neural networks

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

1 Author(s)
Markus, M. ; Illinois State Water Survey, Champaign, IL, USA

Artificial neural networks (ANNs) have a long record of generally promising results in hydrology. The earlier applications were mainly based on the back propagation feedforward method, which often used a lengthy trial-and-error method to determine the final network parameters. An attempt to overcome this shortcoming of the traditional applications is the minimal resource allocation network (MRAN). MRAN is online adaptive method which automatically configures the number of hidden nodes based on the input-output patterns presented to the network. Although MRAN demonstrated superior accuracy and more compact network, when compared with the traditional back propagation method, some additional questions need to be addressed. While the number of hidden nodes is estimated automatically, other user-defined parameters are selected arbitrarily, and adjusted through simulations. This research addresses determining the user-defined parameters prior to the model run. The research also compares MRAN results from two applications, and discusses a pathway towards designing a fully automated MRAN.

Published in:

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on  (Volume:5 )

Date of Conference:

31 July-4 Aug. 2005