This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2009 WRI World Congress on Computer Science and Information Engineering
MEA for Designing Neural Network Weights and Structure Optimization
Los Angeles, California USA
March 31-April 02
ISBN: 978-0-7695-3507-4
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 similartaxis and dissimilation operators of structure optimization are designed. Through similartaxis 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.
Index Terms:
Artificial Neural Network, MEA, Optimization Design, Structure Optimization
Citation:
Tao Fan, Ruiping Wen, "MEA for Designing Neural Network Weights and Structure Optimization," csie, vol. 6, pp.111-115, 2009 WRI World Congress on Computer Science and Information Engineering, 2009
Usage of this product signifies your acceptance of the Terms of Use.