By Topic

Forecasting chaotic time series using neuro-fuzzy approach

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
$33 $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)
A. K. Palit ; NW1/FB1, Bremen Univ., Germany ; D. Popovic

A neuro-fuzzy approach for forecasting of chaotic time series is proposed, based on neuro-implementation of a fuzzy logic system with the Gaussian membership functions. To construct the neuro-fuzzy system that will approximate and forecast the future values of a chaotic time series, the parameters of the membership functions, i.e. the mean (c) and the variance (σ) of the selected Gaussian functions, as well as the center of fuzzy region (yl) are to be adjusted either by backpropagation or the Levenberg-Marquardt training algorithm. To examine the effectiveness of the forecasting method the performance function, like the sum squared errors, mean squared errors, and mean absolute errors, are evaluated. In this way it was shown that the proposed neuro-fuzzy approach is an excellent tool for chaotic time series prediction

Published in:

Neural Networks, 1999. IJCNN '99. International Joint Conference on  (Volume:3 )

Date of Conference:

1999