Cart (Loading....) | Create Account
Close category search window
 

A Hybrid Genetic Learning Algorithm for Pi-Sigma Neural Network and the Analysis of Its Convergence

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)
Yong Nie ; Coll. of Comput. Sci., SuZhou Univ. of Sci. & Technol., Suzhou ; Wei Deng

This paper uses a hybrid genetic learning algorithm to train Pi-sigma neural network and this algorithm was once applied to resolve a function optimizing problem. The hybrid genetic learning algorithm incorporates the stronger global search of genetic algorithm into the stronger local search of flexible polyhedron method, and can search out the global optimum faster than standard genetic algorithm. The experiments show that the hybrid genetic algorithm can achieve better performance. At last, the hybrid genetic algorithm is proved converge to the global optimum with the probability of 1.

Published in:

Natural Computation, 2008. ICNC '08. Fourth International Conference on  (Volume:3 )

Date of Conference:

18-20 Oct. 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.