By Topic

MEA for Designing Neural Network Weights and Structure Optimization

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)
Tao Fan ; Dept. of Math., Shanghai Maritime Univ., Shanghai, China ; Ruiping Wen

For artificial neural network application, its weights and structure optimization design is a key problem. The mind evolutionary algorithm (MEA) is a new evolutionary algorithm which simulates the process of human mind evolution, it has the powerful ability to find global optimum, and it also has much superiority for resolving the problem of numerical and non-numerical optimization. In this paper, a new typical MEA is presented based on the foundational MEA framework to optimize the neural network structure and weights, in which effective similar taxis and dissimilation operators of structure optimization are designed. Through similar taxis operators, the local optimum is found, then exceeding the restriction of local range through dissimilation operators, the global optimum is acquire in global solution space. Finally, simulation results show the effectiveness and correctness of the method.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:6 )

Date of Conference:

March 31 2009-April 2 2009