By Topic

A space contracting particle swarm optimization and its application in investment prediction

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)
Yaping Zhang ; Coll. of Sci., Heilongjiang Inst. of Technol., Harbin, China ; Liwei Zhang

Multi-layer feed-forward network has a good ability of function approximation, but the usual training algorithm, BP algorithm may easily fall into local minimum and it has weak generalization ability. While the space contraction particle swarm optimization (SCPSO) algorithm has a good capability of global search, the training algorithm for multi-layer feed-forward network is constructed on the basis of the SCPSO. Considering the nonlinear feature of the investment issue, a multi-layer feed-forward network model is established. The SCPSO algorithm as learning algorithm is applied to training of multi-layer feed-ward network and then a simulated prediction is made. The comparison of the prediction result between the network based on SCPSO and BP network indicates that the former has high prediction accuracy.

Published in:

Mechatronics and Automation (ICMA), 2012 International Conference on

Date of Conference:

5-8 Aug. 2012