By Topic

Developing Methods to Train Neural Networks for Time-Series Prediction in Environmental Systems

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

4 Author(s)
Jin Liu ; Wuhan Univ., Wuhan ; Yongliang Shi ; Ning Fang ; Keqing He

This paper proposes the local interaction method to train neural networks for predicting future variable values of environmental system. Time-series data including soil, stream water and climatic variables were measured hourly over half of a year at two observation spots in Qingpu district, 45 kilometers west to Shanghai city. Three different methods, including our biologically plausible method, have used the data sets to train neural networks. The temporal pattern recognition capabilities for these methods were compared. Our method was proved more competitive than the other two traditional methods in using large data sets to detect patterns and predict events for complex environmental systems.

Published in:

Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on  (Volume:1 )

Date of Conference:

24-27 Aug. 2007