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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 481919, 6 pages
Research Article

Research and Application of Improved AGP Algorithm for Structural Optimization Based on Feedforward Neural Networks

Computer and Information Engineering College, Guangxi Teachers Education University, Nanning 530023, China

Received 31 May 2014; Revised 18 September 2014; Accepted 7 October 2014

Academic Editor: Yiu-ming Cheung

Copyright © 2015 Ruliang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The adaptive growing and pruning algorithm (AGP) has been improved, and the network pruning is based on the sigmoidal activation value of the node and all the weights of its outgoing connections. The nodes are pruned directly, but those nodes that have internal relation are not removed. The network growing is based on the idea of variance. We directly copy those nodes with high correlation. An improved AGP algorithm (IAGP) is proposed. And it improves the network performance and efficiency. The simulation results show that, compared with the AGP algorithm, the improved method (IAGP) can quickly and accurately predict traffic capacity.