research and application of improved agp algorithm for structural optimization based on feedforward neural networks

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ID: 160772
2015
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Abstract
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.
Reference Key
wang2015mathematicalresearch Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ruliang Wang;Huanlong Sun;Benbo Zha;Lei Wang
Journal journal of power sources
Year 2015
DOI
10.1155/2015/481919
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