We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Dynamic modelling and time-series prediction by incremental growth of lateral delay 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

2 Author(s)
Chan, L. ; Dept. of Electron. & Electr. Eng., Glasgow Univ., UK ; Yun Li

The difficult problems of predicting chaotic time series and modelling chaotic systems is approached using an innovative neural network design. By combining evolutionary techniques with others, good results can be obtained swiftly via incremental network growing. The network architecture and training algorithm make the creation of dynamic models efficient and hassle-free. The network results accurately reflect the outputs of the chaotic systems being modelled and preserve complex attractor structures of these systems

Published in:

Combinations of Evolutionary Computation and Neural Networks, 2000 IEEE Symposium on

Date of Conference: